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  • 51.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security2017In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper (Refereed)
    Abstract [en]

    The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

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    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
  • 52.
    Akalin, Neziha
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kiselev, Andrey
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kristoffersson, Annica
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Loutfi, Amy
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    The Relevance of Social Cues in Assistive Training with a Social Robot2018In: 10th International Conference on Social Robotics, ICSR 2018, Proceedings / [ed] Ge, S.S., Cabibihan, J.-J., Salichs, M.A., Broadbent, E., He, H., Wagner, A., Castro-González, Á., Springer , 2018, p. 462-471Conference paper (Refereed)
    Abstract [en]

    This paper examines whether social cues, such as facial expressions, can be used to adapt and tailor a robot-assisted training in order to maximize performance and comfort. Specifically, this paper serves as a basis in determining whether key facial signals, including emotions and facial actions, are common among participants during a physical and cognitive training scenario. In the experiment, participants performed basic arm exercises with a social robot as a guide. We extracted facial features from video recordings of participants and applied a recursive feature elimination algorithm to select a subset of discriminating facial features. These features are correlated with the performance of the user and the level of difficulty of the exercises. The long-term aim of this work, building upon the work presented here, is to develop an algorithm that can eventually be used in robot-assisted training to allow a robot to tailor a training program based on the physical capabilities as well as the social cues of the users.

  • 53.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    The Relevance of Social Cues in Assistive Training with a Social Robot2018In: 10th International Conference on Social Robotics, ICSR 2018, Proceedings / [ed] Ge, S.S., Cabibihan, J.-J., Salichs, M.A., Broadbent, E., He, H., Wagner, A., Castro-González, Á., Springer, 2018, p. 462-471Conference paper (Refereed)
    Abstract [en]

    This paper examines whether social cues, such as facial expressions, can be used to adapt and tailor a robot-assisted training in order to maximize performance and comfort. Specifically, this paper serves as a basis in determining whether key facial signals, including emotions and facial actions, are common among participants during a physical and cognitive training scenario. In the experiment, participants performed basic arm exercises with a social robot as a guide. We extracted facial features from video recordings of participants and applied a recursive feature elimination algorithm to select a subset of discriminating facial features. These features are correlated with the performance of the user and the level of difficulty of the exercises. The long-term aim of this work, building upon the work presented here, is to develop an algorithm that can eventually be used in robot-assisted training to allow a robot to tailor a training program based on the physical capabilities as well as the social cues of the users.

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    The Relevance of Social Cues in Assistive Training with a Social Robot
  • 54.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People2019In: Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction / [ed] Oliver Korn, Springer, 2019, p. 237-264Chapter in book (Refereed)
    Abstract [en]

    For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

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    Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
  • 55.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    The Influence of Feedback Type in Robot-Assisted Training2019In: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 3, no 4Article in journal (Refereed)
    Abstract [en]

    Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

  • 56.
    Akan, Batu
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Planning and Sequencing Through Multimodal Interaction for Robot Programming2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Over the past few decades the use of industrial robots has increased the efficiency as well as the competitiveness of several sectors. Despite this fact, in many cases robot automation investments are considered to be technically challenging. In addition, for most small and medium-sized enterprises (SMEs) this process is associated with high costs. Due to their continuously changing product lines, reprogramming costs are likely to exceed installation costs by a large margin. Furthermore, traditional programming methods of industrial robots are too complex for most technicians or manufacturing engineers, and thus assistance from a robot programming expert is often needed. The hypothesis is that in order to make the use of industrial robots more common within the SME sector, the robots should be reprogrammable by technicians or manufacturing engineers rather than robot programming experts. In this thesis, a novel system for task-level programming is proposed. The user interacts with an industrial robot by giving instructions in a structured natural language and by selecting objects through an augmented reality interface. The proposed system consists of two parts: (i) a multimodal framework that provides a natural language interface for the user to interact in which the framework performs modality fusion and semantic analysis, (ii) a symbolic planner, POPStar, to create a time-efficient plan based on the user's instructions. The ultimate goal of this work in this thesis is to bring robot programming to a stage where it is as easy as working together with a colleague.This thesis mainly addresses two issues. The first issue is a general framework for designing and developing multimodal interfaces. The general framework proposed in this thesis is designed to perform natural language understanding, multimodal integration and semantic analysis with an incremental pipeline. The framework also includes a novel multimodal grammar language, which is used for multimodal presentation and semantic meaning generation. Such a framework helps us to make interaction with a robot easier and more natural. The proposed language architecture makes it possible to manipulate, pick or place objects in a scene through high-level commands. Interaction with simple voice commands and gestures enables the manufacturing engineer to focus on the task itself, rather than the programming issues of the robot. The second issue addressed is due to inherent characteristics of communication with the use of natural language; instructions given by a user are often vague and may require other actions to be taken before the conditions for applying the user's instructions are met. In order to solve this problem a symbolic planner, POPStar, based on a partial order planner (POP) is proposed. The system takes landmarks extracted from user instructions as input, and creates a sequence of actions to operate the robotic cell with minimal makespan. The proposed planner takes advantage of the partial order capabilities of POP to execute actions in parallel and employs a best-first search algorithm to seek the series of actions that lead to a minimal makespan. The proposed planner can also handle robots with multiple grippers, parallel machines as well as scheduling for multiple product types.

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  • 57.
    Akbari, Aliakbar
    et al.
    Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, Barcelona, Spain.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Rosell, Jan
    Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, Barcelona, Spain.
    Combined heuristic task and motion planning for bi-manual robots2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 6, p. 1575-1590Article in journal (Refereed)
    Abstract [en]

    Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.

  • 58.
    Akin, H. Levent
    et al.
    Bogazici University, Turkey.
    Ito, Nobuhiro
    Aichi Institute of Technology, Japan.
    Jacoff, Adam
    National Institute of Standards, USA.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Pellenz, Johannes
    V&R Vision & Robotics GmbH, Germany.
    Visser, Arnoud
    University of Amsterdam, Holland.
    RoboCup Rescue Robot and Simulation Leagues2013In: The AI Magazine, ISSN 0738-4602, Vol. 34, no 1Article in journal (Refereed)
    Abstract [en]

    The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (e.g. Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.

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  • 59.
    Al Hakim, Ezeddin
    KTH, School of Electrical Engineering and Computer Science (EECS).
    3D YOLO: End-to-End 3D Object Detection Using Point Clouds2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    For safe and reliable driving, it is essential that an autonomous vehicle can accurately perceive the surrounding environment. Modern sensor technologies used for perception, such as LiDAR and RADAR, deliver a large set of 3D measurement points known as a point cloud. There is a huge need to interpret the point cloud data to detect other road users, such as vehicles and pedestrians.

    Many research studies have proposed image-based models for 2D object detection. This thesis takes it a step further and aims to develop a LiDAR-based 3D object detection model that operates in real-time, with emphasis on autonomous driving scenarios. We propose 3D YOLO, an extension of YOLO (You Only Look Once), which is one of the fastest state-of-the-art 2D object detectors for images. The proposed model takes point cloud data as input and outputs 3D bounding boxes with class scores in real-time. Most of the existing 3D object detectors use hand-crafted features, while our model follows the end-to-end learning fashion, which removes manual feature engineering.

    3D YOLO pipeline consists of two networks: (a) Feature Learning Network, an artificial neural network that transforms the input point cloud to a new feature space; (b) 3DNet, a novel convolutional neural network architecture based on YOLO that learns the shape description of the objects.

    Our experiments on the KITTI dataset shows that the 3D YOLO has high accuracy and outperforms the state-of-the-art LiDAR-based models in efficiency. This makes it a suitable candidate for deployment in autonomous vehicles.

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  • 60.
    Al Khatib, Salwa
    et al.
    Mohamed Bin Zayed Univ Artificial Intelligence MB, U Arab Emirates.
    Boudjoghra, Mohamed El Amine
    Mohamed Bin Zayed Univ Artificial Intelligence MB, U Arab Emirates.
    Lahoud, Jean
    Mohamed Bin Zayed Univ Artificial Intelligence MB, U Arab Emirates.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Mohamed Bin Zayed Univ Artificial Intelligence MB, U Arab Emirates.
    3D Instance Segmentation via Enhanced Spatial and Semantic Supervision2023In: 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, IEEE COMPUTER SOC , 2023, p. 541-550Conference paper (Refereed)
    Abstract [en]

    3D instance segmentation has recently garnered increased attention. Typical deep learning methods adopt point grouping schemes followed by hand-designed geometric clustering. Inspired by the success of transformers for various 3D tasks, newer hybrid approaches have utilized transformer decoders coupled with convolutional backbones that operate on voxelized scenes. However, due to the nature of sparse feature backbones, the extracted features provided to the transformer decoder are lacking in spatial understanding. Thus, such approaches often predict spatially separate objects as single instances. To this end, we introduce a novel approach for 3D point clouds instance segmentation that addresses the challenge of generating distinct instance masks for objects that share similar appearances but are spatially separated. Our method leverages spatial and semantic supervision with query refinement to improve the performance of hybrid 3D instance segmentation models. Specifically, we provide the transformer block with spatial features to facilitate differentiation between similar object queries and incorporate semantic supervision to enhance prediction accuracy based on object class. Our proposed approach outperforms existing methods on the validation sets of ScanNet V2 and ScanNet200 datasets, establishing a new state-of-the-art for this task.

  • 61.
    Alaa, Halawani
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Haibo, Li
    School of Computer Science & Communication, Royal Institute of Technology (KTH), Stockholm, Sweden.
    Template-based Search: A Tool for Scene Analysis2016In: 12th IEEE International Colloquium on Signal Processing & its Applications (CSPA): Proceeding, IEEE, 2016, article id 7515772Conference paper (Refereed)
    Abstract [en]

    This paper proposes a simple and yet effective technique for shape-based scene analysis, in which detection and/or tracking of specific objects or structures in the image is desirable. The idea is based on using predefined binary templates of the structures to be located in the image. The template is matched to contours in a given edge image to locate the designated entity. These templates are allowed to deform in order to deal with variations in the structure's shape and size. Deformation is achieved by dividing the template into segments. The dynamic programming search algorithm is used to accomplish the matching process, achieving very robust results in cluttered and noisy scenes in the applications presented.

  • 62.
    Albinsson, John
    et al.
    Lund Univ, Dept Biomed Engn, S-22100 Lund, Sweden..
    Brorsson, Sofia
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Rydén Ahlgren, Åsa
    Lund Univ, Dept Clin Sci, Clin Physiol & Nucl Med Unit, Malmo, Sweden..
    Cinthio, Magnus
    Lund Univ, Dept Biomed Engn, S-22100 Lund, Sweden..
    Improved tracking performance of lagrangian block-matching methodologies using block expansion in the time domain: In silico, phantom and invivo evaluations2014In: Ultrasound in Medicine and Biology, ISSN 0301-5629, E-ISSN 1879-291X, Vol. 40, no 10, p. 2508-2520Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to evaluate tracking performance when an extra reference block is added to a basic block-matching method, where the two reference blocks originate from two consecutive ultrasound frames. The use of an extra reference block was evaluated for two putative benefits: (i) an increase in tracking performance while maintaining the size of the reference blocks, evaluated using in silico and phantom cine loops; (ii) a reduction in the size of the reference blocks while maintaining the tracking performance, evaluated using in vivo cine loops of the common carotid artery where the longitudinal movement of the wall was estimated. The results indicated that tracking accuracy improved (mean - 48%, p<0.005 [in silico]; mean - 43%, p<0.01 [phantom]), and there was a reduction in size of the reference blocks while maintaining tracking performance (mean - 19%, p<0.01 [in vivo]). This novel method will facilitate further exploration of the longitudinal movement of the arterial wall. (C) 2014 World Federation for Ultrasound in Medicine & Biology.

  • 63.
    Albonico, Andrea
    et al.
    Human Vision and Eye Movement Laboratory, Departments of Medicine (Neurology), Ophthalmology and Visual Sciences, Psychology, University of British Columbia, Vancouver, Canada.
    Furubacke, Amanda
    Linköping University, Faculty of Medicine and Health Sciences. Human Vision and Eye Movement Laboratory, Departments of Medicine (Neurology), Ophthalmology and Visual Sciences, Psychology, University of British Columbia, Vancouver, Canada.
    Barton, Jason J. S.
    Human Vision and Eye Movement Laboratory, Departments of Medicine (Neurology), Ophthalmology and Visual Sciences, Psychology, University of British Columbia, Vancouver, Canada.
    Oruc, Ipek
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Canada; Program in Neuroscience, University of British Columbia, Canada.
    Perceptual efficiency and the inversion effect for faces, words and houses2018In: Vision Research, ISSN 0042-6989, E-ISSN 1878-5646, Vol. 153, p. 91-97Article in journal (Refereed)
    Abstract [en]

    Face and visual word recognition are two key forms of expert visual processing. In the domain of object recognition, it has been suggested that expert processing is characterized by the use of different mechanisms from the ones involved in general object recognition. It has been suggested that one traditional marker of expert processing is the inversion effect. To investigate whether face and word recognition differ from general object recognition, we compared the effect of inversion on the perceptual efficiency of face and visual word recognition as well as on the recognition of a third, non-expert object category, houses. From the comparison of identification contrast thresholds to an ideal observer, we derived the efficiency and equivalent input noise of stimulus processing in both upright and inverted orientations. While efficiency reflects the efficacy in sampling the available information, equivalent input noise is associated with the degradation of the stimulus signal within the visual system. We hypothesized that large inversion effects for efficiency and/or equivalent input noise should characterize expert high-level processes, and asked whether this would be true for both faces and words, but not houses. However, we found that while face recognition efficiency was profoundly reduced by inversion, the efficiency of word and house recognition was minimally influenced by the orientation manipulation. Inversion did not affect equivalent input noise. These results suggest that even though faces and words are both considered expert processes, only the efficiency of the mechanism involved in face recognition is sensitive to orientation.

  • 64.
    Alexanderson, Simon
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    O'Sullivan, Carol
    Neff, Michael
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    Mimebot—Investigating the Expressibility of Non-Verbal Communication Across Agent Embodiments2017In: ACM Transactions on Applied Perception, ISSN 1544-3558, E-ISSN 1544-3965, Vol. 14, no 4, article id 24Article in journal (Refereed)
    Abstract [en]

    Unlike their human counterparts, artificial agents such as robots and game characters may be deployed with a large variety of face and body configurations. Some have articulated bodies but lack facial features, and others may be talking heads ending at the neck. Generally, they have many fewer degrees of freedom than humans through which they must express themselves, and there will inevitably be a filtering effect when mapping human motion onto the agent. In this article, we investigate filtering effects on three types of embodiments: (a) an agent with a body but no facial features, (b) an agent with a head only, and (c) an agent with a body and a face. We performed a full performance capture of a mime actor enacting short interactions varying the non-verbal expression along five dimensions (e.g., level of frustration and level of certainty) for each of the three embodiments. We performed a crowd-sourced evaluation experiment comparing the video of the actor to the video of an animated robot for the different embodiments and dimensions. Our findings suggest that the face is especially important to pinpoint emotional reactions but is also most volatile to filtering effects. The body motion, on the other hand, had more diverse interpretations but tended to preserve the interpretation after mapping and thus proved to be more resilient to filtering.

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  • 65.
    Algers, Björn
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Stereo Camera Calibration Accuracy in Real-time Car Angles Estimation for Vision Driver Assistance and Autonomous Driving2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The automotive safety company Veoneer are producers of high end driver visual assistance systems, but the knowledge about the absolute accuracy of their dynamic calibration algorithms that estimate the vehicle’s orientation is limited.

    In this thesis, a novel measurement system is proposed to be used in gathering reference data of a vehicle’s orientation as it is in motion, more specifically the pitch and roll angle of the vehicle. Focus has been to estimate how the uncertainty of the measurement system is affected by errors introduced during its construction, and to evaluate its potential in being a viable tool in gathering reference data for algorithm performance evaluation.

    The system consisted of three laser distance sensors mounted on the body of the vehicle, and a range of data acquisition sequences with different perturbations were performed by driving along a stretch of road in Linköping with weights loaded in the vehicle. The reference data were compared to camera system data where the bias of the calculated angles were estimated, along with the dynamic behaviour of the camera system algorithms. The experimental results showed that the accuracy of the system exceeded 0.1 degrees for both pitch and roll, but no conclusions about the bias of the algorithms could be drawn as there were systematic errors present in the measurements.

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  • 66.
    Alghallabi, Wafa
    et al.
    Mohamed bin Zayed Univ AI, U Arab Emirates.
    Dudhane, Akshay
    Mohamed bin Zayed Univ AI, U Arab Emirates.
    Zamir, Waqas
    Incept Inst AI, U Arab Emirates.
    Khan, Salman
    Mohamed bin Zayed Univ AI, U Arab Emirates; Australian Natl Univ, Australia.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Mohamed bin Zayed Univ AI, U Arab Emirates.
    Accelerated MRI Reconstruction via Dynamic Deformable Alignment Based Transformer2024In: MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, SPRINGER INTERNATIONAL PUBLISHING AG , 2024, Vol. 14348, p. 104-114Conference paper (Refereed)
    Abstract [en]

    Magnetic resonance imaging (MRI) is a slow diagnostic technique due to its time-consuming acquisition speed. To address this, parallel imaging and compressed sensing methods were developed. Parallel imaging acquires multiple anatomy views simultaneously, while compressed sensing acquires fewer samples than traditional methods. However, reconstructing images from undersampled multi-coil data remains challenging. Existing methods concatenate input slices and adjacent slices along the channel dimension to gather more information for MRI reconstruction. Implicit feature alignment within adjacent slices is crucial for optimal reconstruction performance. Hence, we propose MFormer: an accelerated MRI reconstruction transformer with cascading MFormer blocks containing multi-scale Dynamic Deformable Swin Transformer (DST) modules. Unlike other methods, our DST modules implicitly align adjacent slice features using dynamic deformable convolution and extract local non-local features before merging information. We adapt input variations by aggregating deformable convolution kernel weights and biases through a dynamic weight predictor. Extensive experiments on Stanford2D, Stanford3D, and large-scale FastMRI datasets show the merits of our contributions, achieving state-of-the-art MRI reconstruction performance. Our code and models are available at https://github.com/wafaAlghallabi/MFomer.

  • 67.
    Ali, Hani
    et al.
    Halmstad University, School of Information Technology.
    Sunnergren, Pontus
    Halmstad University, School of Information Technology.
    Scenanalys - Övervakning och modellering2021Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Autonomous vehicles can decrease traffic congestion and reduce the amount of traffic related accidents. As there will be millions of autonomous vehicles in the future, a better understanding of the environment will be required. This project aims to create an external automated traffic system that can detect and track 3D objects within a complex traffic situation to later send these objects’ behavior for a larger-scale project that manages to 3D model the traffic situation. The project utilizes Tensorflow framework and YOLOv3 algorithm. The project also utilizes a camera to record traffic situations and a Linux operated computer. Using methods commonly used to create an automated traffic management system was evaluated. The final results show that the system is relatively unstable and can sometimes fail to recognize certain objects. If more images are used for the training process, a more robust and much more reliable system could be developed using a similar methodology. 

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  • 68.
    Ali, Hazrat
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Umander, Johannes
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Rohlén, Robin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 170595-170608Article in journal (Refereed)
    Abstract [en]

    Skeletal muscles are functionally regulated by populations of so-called motor units (MUs). An MU comprises a bundle of muscle fibers controlled by a neuron from the spinal cord. Current methods to diagnose neuromuscular diseases and monitor rehabilitation, and study sports sciences rely on recording and analyzing the bio-electric activity of the MUs. However, these methods provide information from a limited part of a muscle. Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.

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  • 69.
    Aliabad, Fahime Arabi
    et al.
    Yazd Univ, Fac Nat Resources & Desert Studies, Dept Arid Land Management, Yazd 8915818411, Iran..
    Malamiri, Hamid Reza Ghafarian
    Yazd Univ, Dept Geog, Yazd 8915818411, Iran.;Delft Univ Technol, Dept Geosci & Engn, NL-2628 CD Delft, Netherlands..
    Shojaei, Saeed
    Univ Tehran, Fac Nat Resources, Dept Arid & Mt Reg Reclamat, Tehran 1417935840, Iran..
    Sarsangi, Alireza
    Univ Tehran, Fac Geog, Dept Remote Sensing & GIS, Tehran 1417935840, Iran..
    Ferreira, Carla Sofia Santos
    Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, S-10691 Stockholm, Sweden.;Polytech Inst Coimbra, Agr Sch Coimbra, Res Ctr Nat Resources Environm & Soc CERNAS, P-3045601 Coimbra, Portugal..
    Kalantari, Zahra
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Water and Environmental Engineering. Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, S-10691 Stockholm, Sweden..
    Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-22022In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 13, article id 3227Article in journal (Refereed)
    Abstract [en]

    One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.

  • 70.
    AliNazari, Mirian
    Umeå University, Faculty of Teacher Education, Department of Creative Studies.
    Kreativ Uppväxtmiljö: en studie av stadieteorier2007Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    I examensarbetet studerades bildutveckling som även jämförts med författarens egen uppväxtmiljö. Metoden har varit en litteraturstudie som behandlar ämnet estetiska uttrycksformer och kreativ uppväxt. Därtill har en granskning av författarens uppväxtmiljö gällande möjlighet till övande av kreativ förmåga tagits upp i relation till personlig utveckling. Jämförelse har gjorts med stadieteorier om utvecklande av barns bildanvändning. Genom dokumenterade av författarens egna bilder under tidiga år visades bildutveckling i de olika teckningsutvecklingsstadierna. Slutsatsen är att kreativ förmåga påverkas sannolikt av uppfostran fylld med möjligheten att få måla och teckna, något som bildlärare kan utveckla i arbetet med barn. Behov att som blivande lärare integrera bilden i de teoretiska ämnena kan utveckla dessa möjligheter ytterligare.

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  • 71.
    Al-Jaff, Mohammad
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Industrial Engineering and Management.
    Messing With The Gap: On The Modality Gap Phenomenon In Multimodal Contrastive Representation Learning2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In machine learning, a sub-field of computer science, a two-tower architecture model is a specialised type of neural network model that encodes paired data from different modalities (like text and images, sound and video, or proteomics and gene expression profiles) into a shared latent representation space. However, when training these models using a specific contrastive loss function, known as the multimodalinfoNCE loss, seems to often lead to a unique geometric phenomenon known as the modality gap. This gap is a clear geometric separation of the embeddings of the modalities in the joint contrastive latent space. This thesis investigates the modality gap in multimodal machine learning, specifically in two-tower neural networks trained with multimodal-infoNCE loss. We examine the adequacy of the current definition of the modality gap, the conditions under which the modality gap phenomenon manifests, and its impact on representation quality and downstream task performance.

    The approach to address these questions consists of a two-phase experimental strategy. Phase I involves a series of experiments, ranging from toy synthetic simulations to true multimodal machine learning with complex datasets, to explore and characterise the modality gap under varying conditions. Phase II focuses on modifying the modality gap and analysing representation quality, evaluating different loss functions and their impact on the modality gap. This methodical exploration allows us to systematically dissect the emergence and implications of the modality gap phenomenon, providing insights into its impact on downstream tasks, measured with proxy metrics based on semantic clustering in the shared latent representation space and modality-specific linear probe evaluation.

    Our findings reveal that the modality gap definition proposed by W. Liang et al. 2022, is insufficient. We demonstrate that similar modality gap magnitudes can exhibit varying linear separability between modality embeddings in the contrastive latent space and varying embedding topologies, indicating the need for additional metrics to capture the true essence of the gap.

    Furthermore, our experiments show that the temperature hyperparameter in the multimodal infoNCE loss function plays a crucial role in the emergence of the modality gap, and this effect varies with different data sets. This suggests that individual dataset characteristics significantly influence the modality gap's manifestation. A key finding is the consistent emergence of modality gaps with small temperature settings in the fixed temperature mode of the loss function and almost invariably under learned temperature mode settings, regardless of the initial temperature value. Additionally, we observe that the magnitude of the modality gap is influenced by distribution shifts, with the gap magnitude increasing progressively from the training set to the validation set, then to the test set, and finally to more distributionally shifted datasets.

    We discover that the choice of contrastive learning method, temperature settings, and temperature values is crucial in influencing the modality gap. However, reducing the gap does not consistently improve downstream task performance, suggesting its role may be more nuanced than previously understood. This insight indicates that the modality gap might be a geometric by-product of the learning methods rather than a critical determinant of representation quality. Our results encourage the need to reevaluate the modality gap's significance in multimodal contrastive learning, emphasising the importance of dataset characteristics and contrastive learning methodology.

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  • 72.
    Al-Khamisi, Ardoan
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    El Khoury, Christian
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    AI i rekryteringsprocessen: En studie om användningen av AI för CV-analys2024Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The study examines which methods are most suitable for recruitment processes by including three existing artificial intelligence AI-tools as well as a custom-developed prototype. Previous studies have shown that AI can improve recruitment processes by increasing efficiency and reducing biases, but also that there are limitations in how well AI can assess candidate’s competencies. The goal is to determine the most effective AI solutions for matching qualified candidates to leading positions. Opportunities for improvement in speed, accuracy, and quality of the recruitment process have been identified. The focus of this work is on analyzing existing AI-solutions in parallel with the development and testing of a prototype. The prototype has been designed to address the deficiencies identified in existing methods, such as matching keywords between Curriculum Vitae (CV) and job advertisements. This method has limitations in how well it can identify candidate’s real competencies and relevance for the job, which is explored in this study. The results from this study show that AI currently has a limited, but growing significance in recruitment processes. This points to significant potential for AI to provide new solutions that can lead to fairer and more efficient recruitment processes in the future.

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    AI i rekryteringsprocessen
  • 73.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    van de Rijke, Frans M.
    Jahangir Tafrechi, Roos
    Raap, Anton K.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Image Based Measurements of Single Cell mtDNA Mutation Load2007In: Image Analysis, Proceedings / [ed] Ersboll BK, Pedersen KS, 2007, p. 631-640Conference paper (Refereed)
    Abstract [en]

    Cell cultures as well as cells in tissue always display a certain degree of variability, and measurements based on cell averages will miss important information contained in a heterogeneous population. This paper presents automated methods for image based measurements of mitochondiral DNA (mtDNA) mutations in individual cells. The mitochondria are present in the cell’s cytoplasm, and each cytoplasm has to be delineated. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.

  • 74.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    BlobFinder, a tool for fluorescence microscopy image cytometry2009In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, p. 58-65Article in journal (Refereed)
    Abstract [en]

    Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

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  • 75.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Signal Detection in 3D by Stable Wave Signal Verification2009In: Proceedings of SSBA 2009, 2009Conference paper (Other academic)
    Abstract [en]

    Detection and localization of point-source signals is an important task in many image analysis applications. These types of signals can commonly be seen in fluorescent microscopy when studying functions of biomolecules. Visual detection and localization of point-source signals in 3D is limited and time consuming, making automated methods an important task. The 3D Stable Wave Detector (3DSWD) is a new method that combines signal enhancement with a verifier/separator. The verifier/separator examines the intensity gradient around a signal, making the detection less sensitive to noise and better at separating spatially close signals. Conventional methods such as; TopHat, Difference of Gaussian, and Multiscale Product consist only of signal enhancement. In this paper we compare the 3DSWD to these conventional methods with and without the addition of a verifier/separator. We can see that the 3DSWD has the highest robustness to noise among all the methods and that the other methods are improved when a verifier/separator is added.

  • 76. Almansa, A.
    et al.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection2000In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 9, no 12, p. 2027-2042Article in journal (Refereed)
    Abstract [en]

    This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The Scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a Smoothed grey-level version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.

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  • 77. Almansa, Andrés
    et al.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Enhancement of Fingerprint Images by Shape-Adapted Scale-Space Operators1996In: Gaussian Scale-Space Theory. Part I: Proceedings of PhD School on Scale-Space Theory (Copenhagen, Denmark) May 1996 / [ed] J. Sporring, M. Nielsen, L. Florack, and P. Johansen, Springer Science+Business Media B.V., 1996, p. 21-30Chapter in book (Refereed)
    Abstract [en]

    This work presents a novel technique for preprocessing fingerprint images. The method is based on the measurements of second moment descriptors and shape adaptation of scale-space operators with automatic scale selection (Lindeberg 1994). This procedure, which has been successfully used in the context of shape-from-texture and shape from disparity gradients, has several advantages when applied to fingerprint image enhancement, as observed by (Weickert 1995). For example, it is capable of joining interrupted ridges, and enforces continuity of their directional fields.

    In this work, these abovementioned general ideas are applied and extended in the following ways: Two methods for estimating local ridge width are explored and tuned to the problem of fingerprint enhancement. A ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model. This information is used for guiding a scale-selection mechanism, and for spreading the results of shape adaptation into noisy areas.

    The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. To a large extent, the scheme has the desirable property of joining interrupted lines without destroying essential singularities such as branching points. Thus, the result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.

    A detailed experimental evaluation is presented, including a comparison with other techniques. We propose that the techniques presented provide mechanisms of interest to developers of automatic fingerprint identification systems.

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  • 78.
    Almeida, Tiago
    et al.
    Örebro University, School of Science and Technology.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Schreiter, Tim
    Örebro University, School of Science and Technology.
    Zhu, Yufei
    Örebro University, School of Science and Technology.
    Gutiérrez Maestro, Eduardo
    Örebro University, School of Science and Technology.
    Morillo-Mendez, Lucas
    Örebro University, School of Science and Technology.
    Kucner, Tomasz P.
    Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland; FCAI, Finnish Center for Artificial Intelligence, Finland.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Arras, Kai O.
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction2023In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE, 2023, p. 2192-2201Conference paper (Refereed)
    Abstract [en]

    Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

  • 79.
    Almeida, Tiago
    et al.
    Örebro University, School of Science and Technology. IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Santos, Vitor
    IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Lourenco, Bernardo
    IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments2021In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 103, no 1, article id 13Article in journal (Refereed)
    Abstract [en]

    Data Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.

  • 80.
    Almgren, K.M
    et al.
    STFI-Packforsk AB.
    Gamstedt, E.K.
    Department of Polymer and Fibre Technology, Royal Institute of Technology .
    Nygård, P.
    PFI Paper and Fibre Research Institute.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindström, M.
    STFI-Packforsk AB.
    Role of fibre–fibre and fibre–matrix adhesion in stress transfer in composites made from resin-impregnated paper sheets2009In: International Journal of Adhesion and Adhesives, ISSN 0143-7496, E-ISSN 1879-0127, Vol. 29, no 5, p. 551-557Article in journal (Refereed)
    Abstract [en]

    Paper-reinforced plastics are gaining increased interest as packaging materials, where mechanical properties are of great importance. Strength and stress transfer in paper sheets are controlled by fibre–fibre bonds. In paper-reinforced plastics, where the sheet is impregnated with a polymer resin, other stress-transfer mechanisms may be more important. The influence of fibre–fibre bonds on the strength of paper-reinforced plastics was therefore investigated. Paper sheets with different degrees of fibre–fibre bonding were manufactured and used as reinforcement in a polymeric matrix. Image analysis tools were used to verify that the difference in the degree of fibre–fibre bonding had been preserved in the composite materials. Strength and stiffness of the composites were experimentally determined and showed no correlation to the degree of fibre–fibre bonding, in contrast to the behaviour of unimpregnated paper sheets. The degree of fibre–fibre bonding is therefore believed to have little importance in this type of material, where stress is mainly transferred through the fibre–matrix interface.

  • 81.
    Almin, Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Detection of Non-Ferrous Materials with Computer Vision2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In one of the facilities at the Stena Recycling plant in Halmstad, Sweden, about 300 tonnes of metallic waste is processed each day with the aim of sorting out all non-ferrous material. At the end of this process, non-ferrous materials are

    manually sorted out from the ferrous materials. This thesis investigates a computer vision based approach to identify and localize the non-ferrous materials

    and eventually automate the sorting.Images were captured of ferrous and non-ferrous materials. The images areprocessed and segmented to be used as annotation data for a deep convolutionalneural segmentation network. Network models have been trained on different

    kinds and amounts of data. The resulting models are evaluated and tested in ac-cordance with different evaluation metrics. Methods of creating advanced train-ing data by merging imaging information were tested. Experiments with using

    classifier prediction confidence to identify objects of unknown classes were per-formed.

    This thesis shows that it is possible to discern ferrous from non-ferrous mate-rial with a purely vision based system. The thesis also shows that it is possible to

    automatically create annotated training data. It becomes evident that it is possi-ble to create better training data, tailored for the task at hand, by merging image

    data. A segmentation network trained on more than two classes yields lowerprediction confidence for objects unknown to the classifier.Substituting manual sorting with a purely vision based system seems like aviable approach. Before a substitution is considered, the automatic system needsto be evaluated in comparison to the manual sorting.

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    Detection of Non-Ferrous Materials with Computer Vision
  • 82.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning to detect misaligned point clouds2018In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 83.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Eye Detection by Complex Filtering for Periocular Recognition2014In: 2nd International Workshop on Biometrics and Forensics (IWBF2014): Valletta, Malta (27-28th March 2014), Piscataway, NJ: IEEE Press, 2014, article id 6914250Conference paper (Refereed)
    Abstract [en]

    We present a novel system to localize the eye position based on symmetry filters. By using a 2D separable filter tuned to detect circular symmetries, detection is done with a few ID convolutions. The detected eye center is used as input to our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the local power spectrum. This setup is evaluated with two databases of iris data, one acquired with a close-up NIR camera, and another in visible light with a web-cam. The periocular system shows high resilience to inaccuracies in the position of the detected eye center. The density of the sampling grid can also be reduced without sacrificing too much accuracy, allowing additional computational savings. We also evaluate an iris texture matcher based on ID Log-Gabor wavelets. Despite the poorer performance of the iris matcher with the webcam database, its fusion with the periocular system results in improved performance. ©2014 IEEE.

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  • 84.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Periocular Biometrics: Databases, Algorithms and Directions2016In: 2016 4th International Workshop on Biometrics and Forensics (IWBF): Proceedings : 3-4 March, 2016, Limassol, Cyprus, Piscataway, NJ: IEEE, 2016, article id 7449688Conference paper (Refereed)
    Abstract [en]

    Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. Periocular refers to the facial region in the eye vicinity, including eyelids, lashes and eyebrows. It is available over a wide range of acquisition distances, representing a trade-off between the whole face (which can be occluded at close distances) and the iris texture (which do not have enough resolution at long distances). Since the periocular region appears in face or iris images, it can be used also in conjunction with these modalities. Features extracted from the periocular region have been also used successfully for gender classification and ethnicity classification, and to study the impact of gender transformation or plastic surgery in the recognition performance. This paper presents a review of the state of the art in periocular biometric research, providing an insight of the most relevant issues and giving a thorough coverage of the existing literature. Future research trends are also briefly discussed. © 2016 IEEE.

  • 85.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Englund, Cristofer
    RISE Viktoria, Gothenburg, Sweden.
    Expression Recognition Using the Periocular Region: A Feasibility Study2018In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [ed] Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillón-Santana & Richard Chbeir, Los Alamitos: IEEE, 2018, p. 536-541Conference paper (Refereed)
    Abstract [en]

    This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.

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  • 86.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben
    University of Malta, Msida, Malta.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion2016In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), Piscataway: IEEE, 2016, article id 7791208Conference paper (Refereed)
    Abstract [en]

    Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.

  • 87.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
    Hernandez-Diaz, Kevin
    Halmstad University, School of Information Technology.
    Buades, Jose M.
    University of Balearic Islands, Palma, Spain.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Bigun, Josef
    Halmstad University, School of Information Technology.
    An Explainable Model-Agnostic Algorithm for CNN-Based Biometrics Verification2023In: 2023 IEEE International Workshop on Information Forensics and Security (WIFS), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    Abstract [en]

    This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50. © 2023 IEEE.

  • 88.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Hernandez-Diaz, Kevin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ramis, Silvia
    Computer Graphics and Vision and AI Group, University of Balearic Islands, Spain.
    Perales, Francisco J.
    Computer Graphics and Vision and AI Group, University of Balearic Islands, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images2021In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 10, no 5, p. 562-580Article in journal (Refereed)
    Abstract [en]

    We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft-biometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pre-trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesize that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pre-training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition. © The Authors

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    fulltext
  • 89.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Unsupervised construction of 4D semantic maps in a long-term autonomy scenario2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Robots are operating for longer times and collecting much more data than just a few years ago. In this setting we are interested in exploring ways of modeling the environment, segmenting out areas of interest and keeping track of the segmentations over time, with the purpose of building 4D models (i.e. space and time) of the relevant parts of the environment.

    Our approach relies on repeatedly observing the environment and creating local maps at specific locations. The first question we address is how to choose where to build these local maps. Traditionally, an operator defines a set of waypoints on a pre-built map of the environment which the robot visits autonomously. Instead, we propose a method to automatically extract semantically meaningful regions from a point cloud representation of the environment. The resulting segmentation is purely geometric, and in the context of mobile robots operating in human environments, the semantic label associated with each segment (i.e. kitchen, office) can be of interest for a variety of applications. We therefore also look at how to obtain per-pixel semantic labels given the geometric segmentation, by fusing probabilistic distributions over scene and object types in a Conditional Random Field.

    For most robotic systems, the elements of interest in the environment are the ones which exhibit some dynamic properties (such as people, chairs, cups, etc.), and the ability to detect and segment such elements provides a very useful initial segmentation of the scene. We propose a method to iteratively build a static map from observations of the same scene acquired at different points in time. Dynamic elements are obtained by computing the difference between the static map and new observations. We address the problem of clustering together dynamic elements which correspond to the same physical object, observed at different points in time and in significantly different circumstances. To address some of the inherent limitations in the sensors used, we autonomously plan, navigate around and obtain additional views of the segmented dynamic elements. We look at methods of fusing the additional data and we show that both a combined point cloud model and a fused mesh representation can be used to more robustly recognize the dynamic object in future observations. In the case of the mesh representation, we also show how a Convolutional Neural Network can be trained for recognition by using mesh renderings.

    Finally, we present a number of methods to analyse the data acquired by the mobile robot autonomously and over extended time periods. First, we look at how the dynamic segmentations can be used to derive a probabilistic prior which can be used in the mapping process to further improve and reinforce the segmentation accuracy. We also investigate how to leverage spatial-temporal constraints in order to cluster dynamic elements observed at different points in time and under different circumstances. We show that by making a few simple assumptions we can increase the clustering accuracy even when the object appearance varies significantly between observations. The result of the clustering is a spatial-temporal footprint of the dynamic object, defining an area where the object is likely to be observed spatially as well as a set of time stamps corresponding to when the object was previously observed. Using this data, predictive models can be created and used to infer future times when the object is more likely to be observed. In an object search scenario, this model can be used to decrease the search time when looking for specific objects.

    Download full text (pdf)
    Rares_Ambrus_PhD_Thesis
  • 90.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Autonomous meshing, texturing and recognition of objectmodels with a mobile robot2017Conference paper (Refereed)
    Abstract [en]

    We present a system for creating object modelsfrom RGB-D views acquired autonomously by a mobile robot.We create high-quality textured meshes of the objects byapproximating the underlying geometry with a Poisson surface.Our system employs two optimization steps, first registering theviews spatially based on image features, and second aligningthe RGB images to maximize photometric consistency withrespect to the reconstructed mesh. We show that the resultingmodels can be used robustly for recognition by training aConvolutional Neural Network (CNN) on images rendered fromthe reconstructed meshes. We perform experiments on datacollected autonomously by a mobile robot both in controlledand uncontrolled scenarios. We compare quantitatively andqualitatively to previous work to validate our approach.

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    fulltext
  • 91.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Claici, Sebastian
    Wendt, Axel
    Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 749-756Article in journal (Refereed)
    Abstract [en]

    We present an automatic approach for the task of reconstructing a 2-D floor plan from unstructured point clouds of building interiors. Our approach emphasizes accurate and robust detection of building structural elements and, unlike previous approaches, does not require prior knowledge of scanning device poses. The reconstruction task is formulated as a multiclass labeling problem that we approach using energy minimization. We use intuitive priors to define the costs for the energy minimization problem and rely on accurate wall and opening detection algorithms to ensure robustness. We provide detailed experimental evaluation results, both qualitative and quantitative, against state-of-the-art methods and labeled ground-truth data.

  • 92.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ekekrantz, Johan
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario2015In: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, p. 5678-5685Conference paper (Refereed)
    Abstract [en]

    We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.

  • 93.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Unsupervised object segmentation through change detection in a long term autonomy scenario2016In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, p. 1181-1187Conference paper (Refereed)
    Abstract [en]

    In this work we address the problem of dynamic object segmentation in office environments. We make no prior assumptions on what is dynamic and static, and our reasoning is based on change detection between sparse and non-uniform observations of the scene. We model the static part of the environment, and we focus on improving the accuracy and quality of the segmented dynamic objects over long periods of time. We address the issue of adapting the static structure over time and incorporating new elements, for which we train and use a classifier whose output gives an indication of the dynamic nature of the segmented elements. We show that the proposed algorithms improve the accuracy and the rate of detection of dynamic objects by comparing with a labelled dataset.

  • 94.
    Amcoff, Oscar
    Linköping University, Department of Computer and Information Science.
    I don’t know because I’m not a robot: I don’t know because I’m not a robot:A qualitative study exploring moral questions as a way to investigate the reasoning behind preschoolers’ mental state attribution to robots2022Independent thesis Basic level (degree of Bachelor), 12 credits / 18 HE creditsStudent thesis
    Abstract [en]

    Portrayals of artificially intelligent robots are becoming increasingly prevalent in children’s culture. This affects how children perceive robots, which have been found to affect the way children in school understand subjects like technology and programming. Since teachers need to know what influences their pupils' understanding of these subjects, we need to know how children’s preconceptions about robots affect the way they attribute mental states to them. We still know relatively little about how children do this. Based on the above, a qualitative approach was deemed fit. This study aimed to (1) investigate the reasoning and preconceptions underlying children’s mental state attribution to robots, and (2) explore the effectiveness of moral questions as a way to do this. 16 children aged 5- and 6 years old were asked to rate the mental states of four different robots while subsequently being asked to explain their answers. Half of the children were interviewed alone and half in small groups. A thematic analysis was conducted to analyze the qualitative data. Children’s mental state attribution was found to be influenced by preconceptions about robots as a group of entities lacking mental states. Children were found to perceive two robots, Atlas, and Nao, differently in various respects. This was argued to be because the children perceived these robots through archetypal frameworks. Moral questions were found successful as a way to spark reflective reasoning about the mental state attribution in the children. 

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    fulltext
  • 95.
    Amigoni, Francesco
    et al.
    Politecnico di Milano, Milan, Italy.
    Yu, Wonpil
    Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
    Andre, Torsten
    University of Klagenfurt, Klagenfurt, Austria.
    Holz, Dirk
    University of Bonn, Bonn, Germany.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Matteucci, Matteo
    Politecnico di Milano, Milan, Italy.
    Moon, Hyungpil
    Sungkyunkwan University, Suwon, South Korea.
    Yokozuka, Masashi
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Biggs, Geoffrey
    Nat. Inst. of Advanced Industrial Science and Technology, Tsukuba, Japan.
    Madhavan, Raj
    Amrita University, Clarksburg MD, United States of America.
    A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots2018In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 25, no 1, p. 65-76Article in journal (Refereed)
    Abstract [en]

    The availability of environment maps for autonomous robots enables them to complete several tasks. A new IEEE standard, IEEE 1873-2015, Robot Map Data Representation for Navigation (MDR) [15], sponsored by the IEEE Robotics and Automation Society (RAS) and approved by the IEEE Standards Association Standards Board in September 2015, defines a common representation for two-dimensional (2-D) robot maps and is intended to facilitate interoperability among navigating robots. The standard defines an extensible markup language (XML) data format for exchanging maps between different systems. This article illustrates how metric maps, topological maps, and their combinations can be represented according to the standard.

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    IEEE 1873-2015: A Standard for Map Data Representation
  • 96.
    Ammenberg, P.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Analysis of CASI Data - A Case Study From the Archipelago of Stockholm, Sweden2001In: 6th International Conference, Remote Sensing for Marine and Coastal Environments 2000, Charleston, South Caro, 2001, p. 8 pages-Conference paper (Other scientific)
  • 97.
    Ammenberg, P.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Analysis of CASI data - A case study from the archipelago of Stockholm, Sweden2000In: 6th International Conference, Remote Sensing for Marine and CoastalEnvironments, Charleston, South Carolina, USA, 2000Conference paper (Other scientific)
  • 98.
    Ammenberg, P.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Limnology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Flink, P
    Lindell, T.
    Strömbeck, N.
    Bio-optical Modelling Combined with Remote Sensing to Assess Water Quality2002In: International Journal of Remote Sensing, ISSN 0143-1161, Vol. 23, no 8, p. 1621-1638Article in journal (Refereed)
  • 99.
    Ammenberg, Petra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindell, Tommy
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Automated change detection of bleached coral reef areas2002In: Proceedings of 7th International Conference, Remote Sensing for Marine and Coastal Environments, 2002Conference paper (Other academic)
    Abstract [en]

    Recent dramatic bleaching events on coral reefs have enhanced the need for global environmental monitoring. This paper investigates the value of present high spatial resolution satellites to detect coral bleaching using a change detection technique. We compared an IRS LISS-III image taken during the 1998 bleaching event in Belize to images taken before the bleaching event. The sensitivity of the sensors was investigated and a simulation was made to estimate the effect of sub-pixel changes. A manual interpretation of coral bleaching, based on differences between the images, was performed and the outcome were compared to field observations. The spectral characteristics of the pixels corresponding to the field observations and the manually interpreted bleachings have been analysed and compared to pixels from unaffected areas.

  • 100.
    Amundin, Mats
    et al.
    Kolmården Wildlife Park.
    Hållsten, Henrik
    Filosofiska institutionen, Stockholms universitet.
    Eklund, Robert
    Linköping University, Department of Culture and Communication, Language and Culture. Linköping University, Faculty of Arts and Sciences.
    Karlgren, Jussi
    Kungliga Tekniska Högskolan.
    Molinder, Lars
    Carnegie Investment Bank, Swedden.
    A proposal to use distributional models to analyse dolphin vocalisation2017In: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017 / [ed] Angela Dassow, Ricard Marxer & Roger K. Moore, 2017, p. 31-32Conference paper (Refereed)
    Abstract [en]

    This paper gives a brief introduction to the starting points of an experimental project to study dolphin communicative behaviour using distributional semantics, with methods implemented for the large scale study of human language.

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    A proposal to use distributional models to analyse dolphin vocalisation
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