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  • 351.
    Cooney, Martin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ong, Linda
    I+ srl.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Avoiding improper treatment of dementia patients by care robots2019Konferensbidrag (Refereegranskat)
  • 352. Corrigan, L.J.
    et al.
    Basedow, C.A.
    Kuster, D.
    Kappas, A.
    Peters, C.
    Castellano, G.
    Perception Matters! Engagement in Task Orientated Social Robotics2015Konferensbidrag (Refereegranskat)
  • 353.
    Cristea, Alexander
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Klinisk neurofysiologi.
    Karlsson Edlund, Patrick
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys.
    Qaisar, Rizwan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Klinisk neurofysiologi.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Larsson, Lars
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Klinisk neurofysiologi.
    Effects of ageing and gender on the spatial organization of nuclei in single human skeletal muscle cells2009Ingår i: Neuromuscular Disorders, ISSN 0960-8966, E-ISSN 1873-2364, Vol. 19, s. 605-606Artikel i tidskrift (Refereegranskat)
  • 354.
    Cruciani, Silvia
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Smith, Christian
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Integrating Path Planning and Pivoting2018Ingår i: 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Maciejewski, AA Okamura, A Bicchi, A Stachniss, C Song, DZ Lee, DH Chaumette, F Ding, H Li, JS Wen, J Roberts, J Masamune, K Chong, NY Amato, N Tsagwarakis, N Rocco, P Asfour, T Chung, WK Yasuyoshi, Y Sun, Y Maciekeski, T Althoefer, K AndradeCetto, J Chung, WK Demircan, E Dias, J Fraisse, P Gross, R Harada, H Hasegawa, Y Hayashibe, M Kiguchi, K Kim, K Kroeger, T Li, Y Ma, S Mochiyama, H Monje, CA Rekleitis, I Roberts, R Stulp, F Tsai, CHD Zollo, L, IEEE , 2018, s. 6601-6608Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this work we propose a method for integrating motion planning and in-hand manipulation. Commonly addressed as a separate step from the final execution, in-hand manipulation allows the robot to reorient an object within the end-effector for the successful outcome of the goal task. A joint achievement of repositioning the object and moving the manipulator towards its desired final pose saves time in the execution and introduces more flexibility in the system. We address this problem using a pivoting strategy (i.e. in-hand rotation) for repositioning the object and we integrate this strategy with a path planner for the execution of a complex task. This method is applied on a Baxter robot and its efficacy is shown by experimental results.

  • 355.
    Cruciani, Silvia
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Smith, Christian
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Hang, Kaiyu
    Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China.;Hong Kong Univ Sci & Technol, Inst Adv Study, Hong Kong, Peoples R China..
    Dexterous Manipulation Graphs2018Ingår i: 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Maciejewski, AA Okamura, A Bicchi, A Stachniss, C Song, DZ Lee, DH Chaumette, F Ding, H Li, JS Wen, J Roberts, J Masamune, K Chong, NY Amato, N Tsagwarakis, N Rocco, P Asfour, T Chung, WK Yasuyoshi, Y Sun, Y Maciekeski, T Althoefer, K AndradeCetto, J Chung, WK Demircan, E Dias, J Fraisse, P Gross, R Harada, H Hasegawa, Y Hayashibe, M Kiguchi, K Kim, K Kroeger, T Li, Y Ma, S Mochiyama, H Monje, CA Rekleitis, I Roberts, R Stulp, F Tsai, CHD Zollo, L, IEEE , 2018, s. 2040-2047Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation.

  • 356.
    Curic, Vladimir
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Landström, Anders
    Thurley, Matthew J.
    Luengo Hendriks, Cris L.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Adaptive Mathematical Morphology: a survey of the field2014Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 47, s. 18-28Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.

  • 357.
    Curic, Vladimir
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lefèvre, Sébastien
    Luengo Hendriks, Cris L.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Adaptive hit or miss transform2015Ingår i: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer, 2015, s. 741-752Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Hit or Miss Transform is a fundamental morphological operator, and can be used for template matching. In this paper, we present a framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself. We illustrate the difference between the new adaptive Hit or Miss Transform and the classical Hit or Miss Transform. As an example of its usefulness, we show how the new adaptive Hit or Miss Transform can detect particles in single molecule imaging.

  • 358.
    Curic, Vladimir
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Distance measures between digital fuzzy objects and their applicability in image processing2011Ingår i: Combinatorial Image Analysis / [ed] Jake Aggarwal, Reneta Barneva, Valentin Brimkov, Kostadin Koroutchev, Elka Koroutcheva, Springer Berlin/Heidelberg, 2011, s. 385-397Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.

  • 359.
    Curic, Vladimir
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Sladoje, Natasa
    Centre for Mathematics and Statistics, Faculty of Technical Sciences, University of Novi Sad, Serbia.
    The Sum of minimal distances as a useful distance measure for image registration2010Ingår i: Proceedings SSBA 2010 / [ed] Cris Luengo and Milan Gavrilovic, Uppsala: Centre for Image Analysis , 2010, s. 55-58Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    In this paper we study set distances which are used in image registration related problems. We introduced a new distance as a Sum of minimal distances with added linear weights. Linear weights are added in a way to reduce the impact of single outliers. An evaluation of observed distances with respect to applicability to image object registration is performed. A comparative study of set distances with respect to noise sensitivity as well as with respect to translation and rotation of objects in image is presented. Based on our experiments on synthetic images containing various types of noise, we determine that the proposed weighted sum of minimal distances has a good performances for object registration.

  • 360.
    Dahlqvist, Bengt
    Uppsala universitet.
    Decision Making in Image Analysis1983Ingår i: Notes from the Minisymposium on Algorithms and Programs for Computer-Assisted Pattern Recognition: Internal Report 83-11, 1983Konferensbidrag (Övrigt vetenskapligt)
  • 361.
    Dahlqvist, Bengt
    et al.
    Uppsala universitet.
    Bengtsson, Ewert
    Uppsala universitet.
    Eriksson, Olle
    Uppsala universitet.
    Jarkrans, Torsten
    Uppsala universitet.
    Nordin, Bo
    Uppsala universitet.
    Stenkvist, Björn
    Segmentation of Cell Images by Minimum Error Thresholding1981Ingår i: Proceedings of the 2nd Scandinavian Conference on Image Analysis, 1981Konferensbidrag (Refereegranskat)
  • 362.
    Damghanian, Mitra
    et al.
    Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informations- och kommunikationssystem.
    Olsson, Roger
    Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informations- och kommunikationssystem.
    Sjöström, Mårten
    Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för informations- och kommunikationssystem.
    Depth and Angular Resolution in Plenoptic Cameras2015Ingår i: 2015 IEEE International Conference On Image Processing (ICIP), September 2015, IEEE, 2015, s. 3044-3048, artikel-id 7351362Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a model-based approach to extract the depth and angular resolution in a plenoptic camera. Obtained results for the depth and angular resolution are validated against Zemax ray tracing results. The provided model-based approach gives the location and number of the resolvable depth planes in a plenoptic camera as well as the angular resolution with regards to disparity in pixels. The provided model-based approach is straightforward compared to practical measurements and can reflect on the plenoptic camera parameters such as the microlens f-number in contrast with the principal-ray-model approach. Easy and accurate quantification of different resolution terms forms the basis for designing the capturing setup and choosing a reasonable system configuration for plenoptic cameras. Results from this work will accelerate customization of the plenoptic cameras for particular applications without the need for expensive measurements.

  • 363.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Learning Convolution Operators for Visual Tracking2018Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Visual tracking is one of the fundamental problems in computer vision. Its numerous applications include robotics, autonomous driving, augmented reality and 3D reconstruction. In essence, visual tracking can be described as the problem of estimating the trajectory of a target in a sequence of images. The target can be any image region or object of interest. While humans excel at this task, requiring little effort to perform accurate and robust visual tracking, it has proven difficult to automate. It has therefore remained one of the most active research topics in computer vision.

    In its most general form, no prior knowledge about the object of interest or environment is given, except for the initial target location. This general form of tracking is known as generic visual tracking. The unconstrained nature of this problem makes it particularly difficult, yet applicable to a wider range of scenarios. As no prior knowledge is given, the tracker must learn an appearance model of the target on-the-fly. Cast as a machine learning problem, it imposes several major challenges which are addressed in this thesis.

    The main purpose of this thesis is the study and advancement of the, so called, Discriminative Correlation Filter (DCF) framework, as it has shown to be particularly suitable for the tracking application. By utilizing properties of the Fourier transform, a correlation filter is discriminatively learned by efficiently minimizing a least-squares objective. The resulting filter is then applied to a new image in order to estimate the target location.

    This thesis contributes to the advancement of the DCF methodology in several aspects. The main contribution regards the learning of the appearance model: First, the problem of updating the appearance model with new training samples is covered. Efficient update rules and numerical solvers are investigated for this task. Second, the periodic assumption induced by the circular convolution in DCF is countered by proposing a spatial regularization component. Third, an adaptive model of the training set is proposed to alleviate the impact of corrupted or mislabeled training samples. Fourth, a continuous-space formulation of the DCF is introduced, enabling the fusion of multiresolution features and sub-pixel accurate predictions. Finally, the problems of computational complexity and overfitting are addressed by investigating dimensionality reduction techniques.

    As a second contribution, different feature representations for tracking are investigated. A particular focus is put on the analysis of color features, which had been largely overlooked in prior tracking research. This thesis also studies the use of deep features in DCF-based tracking. While many vision problems have greatly benefited from the advent of deep learning, it has proven difficult to harvest the power of such representations for tracking. In this thesis it is shown that both shallow and deep layers contribute positively. Furthermore, the problem of fusing their complementary properties is investigated.

    The final major contribution of this thesis regards the prediction of the target scale. In many applications, it is essential to track the scale, or size, of the target since it is strongly related to the relative distance. A thorough analysis of how to integrate scale estimation into the DCF framework is performed. A one-dimensional scale filter is proposed, enabling efficient and accurate scale estimation.

  • 364.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Visual Tracking2013Självständigt arbete på avancerad nivå (masterexamen), 300 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Visual tracking is a classical computer vision problem with many important applications in areas such as robotics, surveillance and driver assistance. The task is to follow a target in an image sequence. The target can be any object of interest, for example a human, a car or a football. Humans perform accurate visual tracking with little effort, while it remains a difficult computer vision problem. It imposes major challenges, such as appearance changes, occlusions and background clutter. Visual tracking is thus an open research topic, but significant progress has been made in the last few years.

    The first part of this thesis explores generic tracking, where nothing is known about the target except for its initial location in the sequence. A specific family of generic trackers that exploit the FFT for faster tracking-by-detection is studied. Among these, the CSK tracker have recently shown obtain competitive performance at extraordinary low computational costs. Three contributions are made to this type of trackers. Firstly, a new method for learning the target appearance is proposed and shown to outperform the original method. Secondly, different color descriptors are investigated for the tracking purpose. Evaluations show that the best descriptor greatly improves the tracking performance. Thirdly, an adaptive dimensionality reduction technique is proposed, which adaptively chooses the most important feature combinations to use. This technique significantly reduces the computational cost of the tracking task. Extensive evaluations show that the proposed tracker outperform state-of-the-art methods in literature, while operating at several times higher frame rate.

    In the second part of this thesis, the proposed generic tracking method is applied to human tracking in surveillance applications. A causal framework is constructed, that automatically detects and tracks humans in the scene. The system fuses information from generic tracking and state-of-the-art object detection in a Bayesian filtering framework. In addition, the system incorporates the identification and tracking of specific human parts to achieve better robustness and performance. Tracking results are demonstrated on a real-world benchmark sequence.

  • 365.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Bhat, Goutam
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    ECO: Efficient Convolution Operators for Tracking2017Ingår i: Proceedings 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 6931-6939Konferensbidrag (Refereegranskat)
    Abstract [en]

    In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four benchmarks: VOT2016, UAV123, OTB-2015, and Temple-Color. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain in Expected Average Overlap compared to the top ranked method [12] in the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

  • 366.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Häger, Gustav
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Discriminative Scale Space Tracking2017Ingår i: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 39, nr 8, s. 1561-1575Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.

  • 367.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Häger, Gustav
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking2016Ingår i: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1430-1438Konferensbidrag (Refereegranskat)
    Abstract [en]

    Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

  • 368.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Häger, Gustav
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Coloring Channel Representations for Visual Tracking2015Ingår i: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Rasmus R. Paulsen, Kim S. Pedersen, Springer, 2015, Vol. 9127, s. 117-129Konferensbidrag (Refereegranskat)
    Abstract [en]

    Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations \cite{felsberg06PAMI} have shown to provide promising results in many applications ranging from autonomous driving to visual tracking.

    This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking.

    Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.

  • 369.
    Danelljan, Martin
    et al.
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Datorseende.
    Häger, Gustav
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Datorseende.
    Khan, Fahad Shahbaz
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Datorseende.
    Felsberg, Michael
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Datorseende.
    Convolutional Features for Correlation Filter Based Visual Tracking2015Ingår i: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), IEEE conference proceedings, 2015, s. 621-629Konferensbidrag (Refereegranskat)
    Abstract [en]

    Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they mitigate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard handcrafted features. Finally, results comparable to state-of-theart trackers are obtained on all three benchmark datasets.

  • 370.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Häger, Gustav
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Learning Spatially Regularized Correlation Filters for Visual Tracking2015Ingår i: Proceedings of the International Conference in Computer Vision (ICCV), 2015, IEEE Computer Society, 2015, s. 4310-4318Konferensbidrag (Refereegranskat)
    Abstract [en]

    Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model.

    We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.

  • 371.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Heintz, Fredrik
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska högskolan.
    Rudol, Piotr
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska högskolan.
    Wzorek, Mariusz
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska högskolan.
    Kvarnström, Jonas
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska högskolan.
    Doherty, Patrick
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska högskolan.
    A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems2015Ingår i: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, s. 223-237Konferensbidrag (Refereegranskat)
    Abstract [en]

    Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

    In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

  • 372.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Meneghetti, Giulia
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    A Probabilistic Framework for Color-Based Point Set Registration2016Ingår i: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1818-1826Konferensbidrag (Refereegranskat)
    Abstract [en]

    In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

  • 373.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Meneghetti, Giulia
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach2016Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 2016, IEEE, 2016, s. 247-252Konferensbidrag (Refereegranskat)
    Abstract [en]

    3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate high-dimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.

  • 374.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking2016Ingår i: Computer Vision - ECCV 2016, Pt V, Springer, 2016, Vol. 9909, s. 472-488Konferensbidrag (Refereegranskat)
    Abstract [en]

    Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.

  • 375.
    D'Angelo, Mirko
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Caporuscio, Mauro
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).
    Napolitano, Annalisa
    University of Rome 'Tor Vergata', Italy.
    Model-driven Engineering of Decentralized Control in Cyber-Physical Systems2017Ingår i: Proceedings of the 2nd International Workshop on  Foundations and Applications of Self* Systems (FAS*W), IEEE, 2017, s. 7-12Konferensbidrag (Refereegranskat)
    Abstract [en]

    Self-Adaptation is nowadays recognized as an effective approach to manage the complexity and dynamics inherent to cyber-physical systems, which are composed of deeply intertwined physical and software components interacting with each other. A self-Adaptive system typically consists of a managed subsystem and a managing subsystem that implements the adaptation logic by means of the well established MAPE-K control loop. Since in large distributed settings centralized control is hardly adequate to manage the whole system, self-Adaptation should be achieved through collective decentralized control, that is multiple cyber-physical entities must adapt in order to address critical runtime conditions. Developing such systems is challenging, as several dimensions concerning both the cyber-physical system and the decentralized control loop should be considered. To this end, we promote MAPE-K components as first-class modeling abstractions and provide a framework supporting the design, development, and validation of decentralized self-Adaptive cyber-physical systems.

  • 376. Danielsson, Max
    et al.
    Sievert, Thomas
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Rasmusson, Jim
    Sony Mobile Communications AB.
    Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. The implementation is done in OpenCL, and we evaluate the execution time and classification performance. We compare our approach with two other methods, FAST/BRISK and ORB. Performance data is presented for the mobile device Xperia Z3 and the desktop Nvidia GTX 660. Our results indicate that the execution times on the Xperia Z3 are insufficient for real-time applications while desktop execution shows future potential. Classification performance of Harris-Hessian/FREAK indicates that the solution is sensitive to rotation, but superior in scale variant images.

  • 377.
    Danielsson, Oscar
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Shape-based Representations and Boosting for Visual Object Class Detection: Models and methods for representaion and detection in single and multiple views2011Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    Detection of generic visual object classes (i.e. cars, dogs, mugs or people) in images is a task that humans are able to solve with remarkable ease. Unfortunately this has proven a very challenging task for computer vision. Thereason is that different instances of the same class may look very different, i.e. there is a high intra-class variation. There are several causes for intra-class variation; for example (1) the imaging conditions (e.g. lighting and exposure) may change, (2) different objects of the same class typically differ in shape and appearance, (3) the position of the object relative to the camera (i.e. the viewpoint) may change and (4) some objects are articulate and may change pose. In addition the background class, i.e. everything but the target object class, is very large. It is the combination of very high intra-class variation with a large background class that makes generic object class detection difficult.

    This thesis addresses this challenge within the AdaBoost framework. AdaBoost constructs an ensemble of weak classifiers to solve a given classification task and allows great flexibility in the design of these weak classifiers. This thesis proposes several types of weak classifiers that specifically target some of the causes of high intra-class variation. A multi-local classifier is proposed to capture global shape properties for object classes that lack discriminative local features, projectable classifiers are proposed to handle detection from multiple viewpoints and finally gated classifiers are proposed as a generic way to handle high intra-class variation in combination with a large background class.

    All proposed weak classifiers are evaluated on standard datasets to allow performance comparison to other related methods.

  • 378.
    Danielsson, Oscar
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Carlsson, Stefan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Projectable Classifiers for Multi-View Object Class Recognition2011Ingår i: 3rd International IEEE Workshop on 3D Representation and Recognition, 2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a multi-view object class modeling framework based on a simplified camera model and surfels (defined by a location and normal direction in a normalized 3D coordinate system) that mediate coarse correspondences between different views. Weak classifiers are learnt relative to the reference frames provided by the surfels. We describe a weak classifier that uses contour information when its corresponding surfel projects to a contour element in the image and color information when the face of the surfel is visible in the image. We emphasize that these weak classifiers can possibly take many different forms and use many different image features. Weak classifiers are combined using AdaBoost. We evaluate the method on a public dataset [8], showing promising results on categorization, recognition/detection, pose estimation and image synthesis.

  • 379.
    Danielsson, Oscar Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Category-sensitive hashing and bloom filter based descriptors for online keypoint recognition2015Ingår i: 19th Scandinavian Conference on Image Analysis, SCIA 2015, Springer, 2015, s. 329-340Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we propose a method for learning a categorysensitive hash function (i.e. a hash function that tends to map inputs from the same category to the same hash bucket) and a feature descriptor based on the Bloom filter. Category-sensitive hash functions are robust to intra-category variation. In this paper we use them to produce descriptors that are invariant to transformations caused by for example viewpoint changes, lighting variation and deformation. Since the descriptors are based on Bloom filters, they support a ”union” operation. So descriptors of matched features can be aggregated by taking their union.We thus end up with one descriptor per keypoint instead of one descriptor per feature (By keypoint we refer to a world-space reference point and by feature we refer to an image-space interest point. Features are typically observations of keypoints and matched features are observations of the same keypoint). In short, the proposed descriptor has data-defined invariance properties due to the category-sensitive hashing and is aggregatable due to its Bloom filter inheritance. This is useful whenever we require custom invariance properties (e.g. tracking of deformable objects) and/or when we make multiple observations of each keypoint (e.g. tracking, multi-view stereo or visual SLAM).

  • 380.
    Daoutis, Marios
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Knowledge based perceptual anchoring: grounding percepts to concepts in cognitive robots2013Ingår i: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, s. 1-4Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Perceptual anchoring is the process of creating and maintaining a connection between the sensor data corresponding to a physical object and its symbolic description. It is a subset of the symbol grounding problem, introduced by Harnad (Phys. D, Nonlinear Phenom. 42(1–3):335–346, 1990) and investigated over the past years in several disciplines including robotics. This PhD dissertation focuses on a method for grounding sensor data of physical objects to the corresponding semantic descriptions, in the context of cognitive robots where the challenge is to establish the connection between percepts and concepts referring to objects, their relations and properties. We examine how knowledge representation can be used together with an anchoring framework, so as to complement the meaning of percepts while supporting better linguistic interaction with the use of the corresponding concepts. The proposed method addresses the need to represent and process both perceptual and semantic knowledge, often expressed in different abstraction levels, while originating from different modalities. We then focus on the integration of anchoring with a large scale knowledge base system and with perceptual routines. This integration is applied in a number of studies, where in the context of a smart home, several evaluations spanning from spatial and commonsense reasoning to linguistic interaction and concept acquisition.

  • 381.
    Daoutis, Marios
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Coradeschi, Silvia
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Loutfi, Amy
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Towards concept anchoring for cognitive robots2012Ingår i: Intelligent Service Robotics, ISSN 1861-2784, Vol. 5, nr 4, s. 213-228Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.

  • 382. Darrell, T. J.
    et al.
    Yeh, T.
    Tollmar, Konrad
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikationssystem, CoS. KTH, Skolan för informations- och kommunikationsteknik (ICT), Centra, KTH Center för Trådlösa System, Wireless@kth.
    Photo-based mobile deixis system and related techniques2004Patent (Övrig (populärvetenskap, debatt, mm))
  • 383.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Valencia, Rafael
    Carnegie Mellon University, Pittsburgh, USA.
    Philippsen, Roland
    Google Inc..
    Bosshard, Pascal
    ETH Zürich, Zürich, Switzerland.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Gradient Based Path Optimization Method for Autonomous Driving2017Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), [Piscataway, NJ]: IEEE, 2017, s. 4501-4508Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper discusses the possibilities of extending and adapting the CHOMP motion planner to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck. Integration experiments were conducted in a real-time environment with the developed autonomous truck. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles. ©2017 IEEE

  • 384.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Valencia, Rafael
    Carnegie Mellon University, Pittsburgh, USA.
    Philippsen, Roland
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, USA.
    Local Path Optimizer for an Autonomous Truck in a Harbour Scenario2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recently, functional gradient algorithms like CHOMP have been very successful in producing locally optimal motion plans for articulated robots. In this paper, we have adapted CHOMP to work with a non-holonomic vehicle such as an autonomous truck with a single trailer and a differential drive robot. An extended CHOMP with rolling constraints have been implemented on both of these setup which yielded feasible curvatures. This paper details the experimental integration of the extended CHOMP motion planner with the sensor fusion and control system of an autonomous Volvo FH-16 truck. It also explains the experiments conducted on the differential-drive robot. Initial experimental investigations and results conducted in a real-world environment show that CHOMP can produce smooth and collision-free trajectories for mobile robots and vehicles as well. In conclusion, this paper discusses the feasibility of employing CHOMP to mobile robots.

  • 385. Davies, A.
    et al.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Dalton, C.
    Campbell, N.
    Generating 3D Morphable Model parameters for facial tracking: Factorising identity and expression2012Ingår i: GRAPP 2012 IVAPP 2012 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications, 2012, s. 309-318Konferensbidrag (Refereegranskat)
    Abstract [en]

    The ability to factorise parameters into identity and expression parameters is highly desirable in facial tracking as it requires only the identity parameters to be set in the initial frame leaving the expression parameters to be adjusted in subsequent frames. In this paper we introduce a strategy for creating parameters for a data-driven 3D Morphable Model (3DMM) which are able to separately model the variance due to identity and expression found in the training data. We present three factorisation schemes and evaluate their appropriateness for tracking by comparing the variances between the identity coefficients and expression coefficients when fitted to data of individuals performing different facial expressions.

  • 386.
    De Cubber, Geert
    et al.
    Royal Military Academy, Belgium.
    Doroftei, Daniela
    Royal Military Academy, Belgium.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Sirakoulis, Georgios Ch.
    Electrical and Computer Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Stereo-based terrain traversability analysis for robot navigation2009Ingår i: IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    Outdoor mobile robots, which have to navigate autonomously in a totally nstructured environment need to auto-determine the suitability of the terrain around them for traversal. Traversability estimation is a challenging problem, as the traversability is a complex function of both the terrain characteristics, such as slopes, vegetation, rocks, etc and the robot mobility characteristics, i.e. locomotion method, wheel properties, etc. In this paper, we present an approach where a classification of the terrain in the classes “traversable” and “obstacle” is performed using only stereo vision as input data. In a first step, high-quality stereo disparity maps are calculated by a fast and robust algorithm. This stereo algorithm is explained in section 3 of this paper. Using this stereo depth information, the terrain classification is performed, based upon the analysis of the so-called "v-disparity" image which provides a representation of the geometric content of the scene. Using this method, it is possible to detect non-traversable terrain items (obstacles) even in the case of partial occlusion and without any explicit extraction of coherent structures or any a priori knowledge of the environment. The sole algorithm parameter is a single factor which takes into account the robot mobility characteristics. This terrain traversability estimation algorithm is explained in section 4. The stereo disparity mapping and terrain traversability estimation processes are integrated in an autonomous robot control architecture, proving that the algorithms allow real-time robot control. The results of experiments with this robot navigating on rough outdoor terrain are presented in section 5.

  • 387.
    De Cubber, Geert
    et al.
    Royal Military Academy, Belgium.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Sirakoulis, Georgios Ch.
    Electrical and Computer Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Intelligent robots need intelligent vision: Visual 3D perception2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    Contemporary autonomous robots are generally equipped with an abundance of sensors like for example GPS, Laser, ultrasound sensors, etc to be able to navigate in an environment. However, this stands in contrast to the ultimate biological example for these robots: us humans. Indeed, humans seem perfectly capable to navigate in a complex, dynamic environment using primarily vision as a sensing modality. This observation inspired us to investigate visually guided intelligent mobile robots. In order to understand and reason about its environment, an intelligent robot needs to be aware of the three-dimensional status of this environment. The problem with vision, though, is that the perceived image is a two-dimensional projection of the 3D world. Recovering 3D-information has been in the focus of attention of the computer vision community for a few decades now, yet no all-satisfying method has been found so far. Most attention in this area has been on stereo-vision based methods, which use the displacement of objects in two (or more) images. Where stereo vision must be seen as a spatial integration of multiple viewpoints to recover depth, it is also possible to perform a temporal integration. The problem arising in this situation is known as the "Structure from Motion" (SfM) problem and deals with extracting 3-dimensional information about the environment from the motion of its projection onto a two-dimensional surface. In this paper, we investigate the possibilities of stereo and structure from motion approaches. It is not the aim to compare both theories of depth reconstruction with the goal of designating a winner and a loser. Both methods are capable of providing sparse as well as dense 3D reconstructions and both approaches have their merits and defects. The thorough, year-long research in the field indicates that accurate depth perception requires a combination of methods rather than a sole one. In fact, cognitive research has shown that the human brain uses no less than 12 different cues to estimate depth. Therefore, we also finally introduce in a following section a methodology to integrate stereo and structure from motion.

  • 388. De Nicola, Giuseppe
    et al.
    Flammini, Francesco
    Mazzocca, Nicola
    Orazzo, Antonio
    Model-based functional verification & validation of complex train control systems: an on-board system testing case-study2005Ingår i: Archives of Transport, ISSN 0866-9546, Vol. 17, nr 3-4, s. 163-176Artikel i tidskrift (Refereegranskat)
  • 389.
    de Pierrefeu, Amicie
    et al.
    NeuroSpin, CEA, Gif-sur-Yvette, France.
    Löfstedt, Tommy
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Laidi, C.
    NeuroSpin, CEA, Gif-sur-Yvette, France; Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Hadj-Selem, Fouad
    Energy Transition Institute: VeDeCoM, Versailles, France.
    Bourgin, Julie
    Department of Psychiatry, Louis-Mourier Hospital, AP-HP, Colombes, France; INSERM U894, Centre for Psychiatry and Neurosciences, Paris, France.
    Hajek, Tomas
    Department of Psychiatry, Dalhousie University, Halifax, NS, Canada; National Institute of Mental Health, Klecany, Czech Republic.
    Spaniel, Filip
    National Institute of Mental Health, Klecany, Czech Republic.
    Kolenic, Marian
    National Institute of Mental Health, Klecany, Czech Republic.
    Ciuciu, Philippe
    NeuroSpin, CEA, Gif-sur-Yvette, France; INRIA, CEA, Parietal team, University of Paris-Saclay, France.
    Hamdani, Nora
    Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Leboyer, Marion
    Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Fovet, Thomas
    Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France.
    Jardri, Renaud
    INRIA, CEA, Parietal team, University of Paris-Saclay, France; Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France.
    Houenou, Josselin
    NeuroSpin, CEA, Gif-sur-Yvette, France; Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Gif-sur-Yvette, France.
    Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine-learning with structured sparsity2018Ingår i: Acta Psychiatrica Scandinavica, ISSN 0001-690X, E-ISSN 1600-0447, Vol. 138, s. 571-580Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    ObjectiveStructural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross‐sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings’ reproducibility.

    MethodWe propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross‐site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first‐episode patients.

    ResultsMachine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first‐episode psychosis patients (73% accuracy).

    ConclusionThese results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.

  • 390. Degerman, Johan
    et al.
    Althoff, Karin
    Thorlin, Thorleif
    Wählby, Carolina
    Uppsala universitet, Fakultetsövergripande enheter, Centrum för bildanalys. Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Karlsson, Patrick
    Uppsala universitet, Fakultetsövergripande enheter, Centrum för bildanalys. Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Bengtsson, Ewert
    Uppsala universitet, Fakultetsövergripande enheter, Centrum för bildanalys. Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Gustavsson, Tomas
    Modeling stem cell migration by Hidden Markov2004Ingår i: Proceedings of the Swedish Symposium on Image Analysis, SSBA 2004, 2004, s. 122-125Konferensbidrag (Övrigt vetenskapligt)
  • 391.
    Dehlin, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Löf, Joakim
    Linköpings universitet, Institutionen för systemteknik.
    Dynamic Infrared Simulation: A Feasibility Study of a Physically Based Infrared Simulation Model2006Självständigt arbete på grundnivå (yrkesexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    The increased usage of infrared sensors by pilots has created a growing demand for simulated environments based on infrared radiation. This has led to an increased need for Saab to refine their existing model for simulating real-time infrared imagery, resulting in the carrying through of this thesis. Saab develops the Gripen aircraft, and they provide training simulators where pilots can train in a realistic environment. The new model is required to be based on the real-world behavior of infrared radiation, and furthermore, unlike Saab's existing model, have dynamically changeable attributes.

    This thesis seeks to develop a simulation model compliant with the requirements presented by Saab, and to develop the implementation of a test environment demonstrating the features and capabilities of the proposed model. All through the development of the model, the pilot training value has been kept in mind.

    The first part of the thesis consists of a literature study to build a theoretical base for the rest of the work. This is followed by the development of the simulation model itself and a subsequent implementation thereof. The simulation model and the test implementation are evaluated as the final step conducted within the framework of this thesis.

    The main conclusions of this thesis first of all includes that the proposed simulation model does in fact have its foundation in physics. It is further concluded that certain attributes of the model, such as time of day, are dynamically changeable as requested. Furthermore, the test implementation is considered to have been feasibly integrated with the current simulation environment.

    A plan concluding how to proceed has also been developed. The plan suggests future work with the proposed simulation model, since the evaluation shows that it performs well in comparison to the existing model as well as other products on the market.

  • 392.
    Delic, Marija
    et al.
    University of Novi Sad, Faculty of technical sciences.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    αLBP – a novel member of the Local Binary Pattern family based on α-cutting2015Ingår i: Proc. 9th International Symposium on Image and Signal Processing and Analysis, IEEE , 2015, s. 13-18Konferensbidrag (Refereegranskat)
    Abstract [en]

    Local binary pattern (LBP) descriptors have been popular in texture classification in recent years. They were introduced as descriptors of local image texture and their histograms are shown to be well performing texture features. In this paper we introduce two new LBP descriptors, αLBP and its improved variant IαLBP. We evaluate their performance in classification by comparing them with some of the existing LBP descriptors - LBP, ILBP, shift LBP (SLBP) and with one ternary descriptor - LTP. The texture descriptors are evaluated on three datasets - KTH-TIPS2b, UIUC and Virus texture dataset. The novel descriptor outperforms the other descriptors on two datasets, KTH-TIPS2b and Virus, and is tied for first place with ILBP on the UIUC dataset.

  • 393.
    Della Corte, Bartolomeo
    et al.
    Department of Computer, Control, and Management Engineering “Antonio Ruberti” Sapienza, University of Rome, Rome, Italy.
    Andreasson, Henrik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Stoyanov, Todor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Grisetti, Giorgio
    Department of Computer, Control, and Management Engineering “Antonio Ruberti” Sapienza, University of Rome, Rome, Italy.
    Unified Motion-Based Calibration of Mobile Multi-Sensor Platforms With Time Delay Estimation2019Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, nr 2, s. 902-909Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it, and their time delays. In this letter, we present a unified pipeline for motion-based calibration of mobile platforms equipped with multiple heterogeneous sensors. We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters, and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value. We release an open-source version of our framework to the community.

  • 394. Deshmukh, A.
    et al.
    Jones, A.
    Janarthanam, S.
    Foster, M.-E.
    Ribeiro, T.
    Corrigan, L.J.
    Aylett, R.
    Paiva, A.
    Papadopoulos, F.
    Castellano, G.
    Empathic Robotic Tutors: Map Guide2015Konferensbidrag (Refereegranskat)
  • 395. Deshmukh, A.
    et al.
    Jones, A.
    Janarthanam, S.
    Hastie, H.
    Ribeiro, T.
    Aylett, R.
    Paiva, A.
    Castellano, G.
    An Empathic Robotic Tutor in a Map Application2015Konferensbidrag (Refereegranskat)
  • 396.
    Detry, Renaud
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Madry, Marianna
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Learning a dictionary of prototypical grasp-predicting parts from grasping experience2013Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, s. 601-608Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

  • 397.
    Divak, Martin
    Luleå tekniska universitet, Institutionen för system- och rymdteknik.
    Simulated SAR with GIS data and pose estimation using affine projection2017Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Pilots or autonomous aircraft need to know where they are in relation to the environment. On board aircraft there are inertial sensors that are prone to drift which requires corrections by referencing against known items, places, or signals. One such method of referencing is with global navigation satellite systems, and others, that are highlighted in this work, are based on using visual sensors. In particular the use of Synthetic Aperture Radar is emerging as a viable alternative.

    To use radar images in qualitative or quantitative analysis they must be registered with geographical information. Position data on an aircraft or spacecraft is not sufficient to determine with certainty what or where it is one is looking at in a radar image without referencing other images over the same area. It is demonstrated in this thesis that a digital elevation model can be split up and classified into different types of radar scatterers. Different parts of the terrain yielding different types of echoes increases the amount of radar specific characteristics in simulated reference images.

    This work also presents an interpretation of the imaging geometry of SAR such that existing methods in Computer Vision may be used to estimate the position from which a radar image has been taken. This is a direct image matching without requiring registration that is necessary for other proposals of SAR-based navigation solutions. By determination of position continuously from radar images, aircraft could navigate independently of day light, weather, and satellite data.

  • 398.
    Djikic, Addi
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Segmentation and Depth Estimation of Urban Road Using Monocular Camera and Convolutional Neural Networks2018Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Deep learning för säkra autonoma transportsystem framträder mer och mer inom forskning och utveckling. Snabb och robust uppfattning om miljön för autonoma fordon kommer att vara avgörande för framtida navigering inom stadsområden med stor trafiksampel.

    I denna avhandling härleder vi en ny form av ett neuralt nätverk som vi kallar AutoNet. Där nätverket är designat som en autoencoder för pixelvis djupskattning av den fria körbara vägytan för stadsområden, där nätverket endast använder sig av en monokulär kamera och dess bilder. Det föreslagna nätverket för djupskattning hanteras som ett regressions problem. AutoNet är även konstruerad som ett klassificeringsnätverk som endast ska klassificera och segmentera den körbara vägytan i realtid med monokulärt seende. Där detta är hanterat som ett övervakande klassificerings problem, som även visar sig vara en mer simpel och mer robust lösning för att hitta vägyta i stadsområden.

    Vi implementerar även ett av de främsta neurala nätverken ENet för jämförelse. ENet är utformat för snabb semantisk segmentering i realtid, med hög prediktions- hastighet. Evalueringen av nätverken visar att AutoNet utklassar ENet i varje prestandamätning för noggrannhet, men visar sig vara långsammare med avseende på antal bilder per sekund. Olika optimeringslösningar föreslås för framtida arbete, för hur man ökar nätverk-modelens bildhastighet samtidigt som man behåller robustheten.All träning och utvärdering görs på Cityscapes dataset. Ny data för träning samt evaluering för djupskattningen för väg skapas med ett nytt tillvägagångssätt, genom att kombinera förberäknade djupkartor med semantiska etiketter för väg. Datainsamling med ett Scania-fordon utförs även, monterad med en monoculär kamera för att testa den slutgiltiga härleda modellen.

    Det föreslagna nätverket AutoNet visar sig vara en lovande topp-presterande modell i fråga om djupuppskattning för väg samt vägklassificering för stadsområden.

  • 399.
    Dornaika, Fadi
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildkodning. Linköpings universitet, Tekniska högskolan.
    Ahlberg, Jörgen
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Face Model Adaptation for Tracking and Active Appearance Model Training2003Ingår i: Proceedings of the British Machine Vision Conference / [ed] Richard Harvey and Andrew Bangham, 2003, s. 57.1-57.10Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    In this paper, we consider the potentialities of adapting a 3D deformable face model to video sequences. Two adaptation methods are proposed. The first method computes the adaptation using a locally exhaustive and directed search in the parameter space. The second method decouples the estimation of head and facial feature motion. It computes the 3D head pose by combining: (i) a robust feature-based pose estimator, and (ii) a global featureless criterion. The facial animation parameters are then estimated with a combined exhaustive and directed search. Tracking experiments and performance evaluation demonstrate the feasibility and usefulness of the developed methods. These experiments also show that the proposed methods can outperform the adaptation based on a directed continuous search.

  • 400.
    Dornaika, Fadi
    et al.
    Laboratoire Heudiasyc, Université de Technologie de Compiègne, France.
    Ahlberg, Jörgen
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Fast and Reliable Active Appearance Model Search for 3D Face Tracking2004Ingår i: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, ISSN 1083-4419, E-ISSN 1941-0492, Vol. 34, nr 4, s. 1838-1853Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper addresses the three-dimensional (3-D) tracking of pose and animation of the human face in monocular image sequences using active appearance models. The major problem of the classical appearance-based adaptation is the high computationaltimeresultingfrom theinclusionofasynthesisstep in the iterative optimization. Whenever the dimension of the face space is large, a real-time performance cannot be achieved. In this paper, we aim at designing a fast and stable active appearance model search for 3-D face tracking. The main contribution is a search algorithm whose CPU-time is not dependent on the dimension of the face space. Using this algorithm, we show that both the CPU-time and the likelihood of a nonaccurate tracking are reduced. Experiments evaluating the effectiveness of the proposed algorithm are reported, as well as method comparison and tracking synthetic and real image sequences.

567891011 351 - 400 av 1863
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