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  • 401. Lopez-Nicolas, G.
    et al.
    Sagues, C.
    Guerrero, J. J.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Jensfelt, Patric
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Switching visual control based on epipoles for mobile robots2008Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 56, nr 7, s. 592-603Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we present a Visual control approach consisting in a switching control scheme based on the epipolar geometry. The method facilitates a classical teach-by-showing approach where a reference image is used to control the robot to the desired pose (position and orientation). As a result of our proposal a mobile robot carries out a smooth trajectory towards the target and the epipolar geometry model is used through the whole motion. The control scheme developed considers the motion constraints of the mobile platform in a framework based on the epipolar geometry that does not rely on artificial markers or specific models of the environment. The proposed method is designed in order to cope with the degenerate estimation case of the epipolar geometry with short baseline. Experimental evaluation has been performed in realistic indoor and outdoor settings.

  • 402.
    Losch, Max
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Detection and Segmentation of Brain Metastases with Deep Convolutional Networks2015Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    As deep convolutional networks (ConvNets) reach spectacular results on a multitude of computer vision tasks and perform almost as well as a human rater on the task of segmenting gliomas in the brain, I investigated the applicability for detecting and segmenting brain metastases. I trained networks with increasing depth to improve the detection rate and introduced a border-pair-scheme to reduce oversegmentation. A constraint on the time for segmenting a complete brain scan required the utilization of fully convolutional networks which reduced the time from 90 minutes to 40 seconds. Despite some present noise and label errors in the 490 full brain MRI scans, the final network achieves a true positive rate of 82.8% and 0.05 misclassifications per slice where all lesions greater than 3 mm have a perfect detection score. This work indicates that ConvNets are a suitable approach to both detect and segment metastases, especially as further architectural extensions might improve the predictive performance even more.

  • 403.
    Loy, Gareth
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Eklundh, Jan-Olof
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Detecting symmetry and symmetric constellations of features2006Ingår i: COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS / [ed] Leonardis, A; Bischof, H; Pinz, A, 2006, Vol. 3952, s. 508-521Konferensbidrag (Refereegranskat)
    Abstract [en]

    A novel and efficient method is presented for grouping feature points on the basis of their underlying symmetry and characterising the symmetries present in an image. We show how symmetric pairs of features can be efficiently detected, how the symmetry bonding each pair is extracted and evaluated, and how these can be grouped into symmetric constellations that specify the dominant symmetries present in the image. Symmetries over all orientations and radii are considered simultaneously, and the method is able to detect local or global symmetries, locate symmetric figures in complex backgrounds, detect bilateral or rotational symmetry, and detect multiple incidences of symmetry.

  • 404.
    Loy, Gareth
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Eriksson, Martin
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Sullivan, Josephine
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Carlsson, Stefan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Monocular 3D reconstruction of human motion in long action sequences2004Ingår i: COMPUTER VISION: ECCV 2004, PT 4, BERLIN: SPRINGER , 2004, Vol. 2034, s. 442-455Konferensbidrag (Refereegranskat)
    Abstract [en]

    A novel algorithm is presented for the 3D reconstruction of human action in long (> 30 second) monocular image sequences. A sequence is represented by a small set of automatically found representative keyframes. The skeletal joint positions are manually located in each keyframe and mapped to all other frames in the sequence. For each keyframe a 3D key pose is created, and interpolation between these 3D body poses, together with the incorporation of limb length and symmetry constraints, provides a smooth initial approximation of the 3D motion. This is then fitted to the image data to generate a realistic 3D reconstruction. The degree of manual input required is controlled by the diversity of the sequence's content. Sports' footage is ideally suited to this approach as it frequently contains a limited number of repeated actions. Our method is demonstrated on a long (36 second) sequence of a woman playing tennis filmed with a non-stationary camera. This sequence required manual initialisation on < 1.5% of the frames, and demonstrates that the system can deal with very rapid motion, severe self-occlusions, motion blur and clutter occurring over several concurrent frames. The monocular 3D reconstruction is verified by synthesising a view from the perspective of a 'ground truth' reference camera, and the result is seen to provide a qualitatively accurate 3D reconstruction of the motion.

  • 405. Lundberg, I.
    et al.
    Björkman, Mårten
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Intrinsic camera and hand-eye calibration for a robot vision system using a point marker2015Ingår i: IEEE-RAS International Conference on Humanoid Robots, IEEE Computer Society, 2015, s. 59-66Konferensbidrag (Refereegranskat)
    Abstract [en]

    Accurate robot camera calibration is a requirement for vision guided robots to perform precision assembly tasks. In this paper, we address the problem of doing intrinsic camera and hand-eye calibration on a robot vision system using a single point marker. This removes the need for using bulky special purpose calibration objects, and also facilitates on line accuracy checking and re-calibration when needed, without altering the robots production environment. The proposed solution provides a calibration routine that produces high quality results on par with the robot accuracy and completes a calibration in 3 minutes without need of manual intervention. We also present a method for automatic testing of camera calibration accuracy. Results from experimental verification on the dual arm concept robot FRIDA are presented.

  • 406.
    Luo, Guoliang
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Representing actions with Kernels2011Ingår i: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, s. 2028-2035Konferensbidrag (Refereegranskat)
    Abstract [en]

    A long standing research goal is to create robots capable of interacting with humans in dynamic environments.To realise this a robot needs to understand and interpret the underlying meaning and intentions of a human action through a model of its sensory data. The visual domain provides a rich description of the environment and data is readily available in most system through inexpensive cameras. However, such data is very high-dimensional and extremely redundant making modeling challenging.Recently there has been a significant interest in semantic modeling from visual stimuli. Even though results are encouraging available methods are unable to perform robustly in realworld scenarios.In this work we present a system for action modeling from visual data by proposing a new and principled interpretation for representing semantic information. The representation is integrated with a real-time segmentation. The method is robust and flexible making it applicable for modeling in a realistic interaction scenario which demands handling noisy observations and require real-time performance. We provide extensive evaluation and show significant improvements compared to the state-of-the-art.

  • 407. Luo, J.
    et al.
    Pronobis, Andrzej
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Caputo, B.
    Jensfelt, Patric
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Incremental learning for place recognition in dynamic environments2007Ingår i: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, IEEE , 2007, s. 721-728Konferensbidrag (Refereegranskat)
    Abstract [en]

    Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach.

  • 408.
    Luo, Jie
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Pronobis, Andrzej
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Caputo, Barbara
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    SVM-based Transfer of Visual Knowledge Across Robotic Platforms2007Ingår i: Proceedings of the 5th International Conference on Computer Vision Systems (ICVS’07), Applied Computer Science Group, Bielefeld University, Germany , 2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an SVM-based algorithm for the transfer of knowledge across robot platforms aiming to perform the same task. Our method exploits efficiently the transferred knowledge while updating incrementally the internal representation as new information is available. The algorithm is adaptive and tends to privilege new data when building the SV solution. This prevents the old knowledge to nest into the model and eventually become a possible source of misleading information. We tested our approach in the domain of vision-based place recognition. Extensive experiments show that using transferred knowledge clearly pays off in terms of performance and stability of the solution.

  • 409. López-Nicolás, G
    et al.
    Sagüés, C.
    Guerrero, J.
    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.
    Jensfelt, Patric
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Nonholonomic epipolar visual servoing2006Ingår i: 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), New York: IEEE , 2006, s. 2378-2384Konferensbidrag (Refereegranskat)
    Abstract [en]

    A significant amount of work has been reported in the area of visual servoing during the last decade. However, most of the contributions are applied in cases of holonomic robots. More recently, the use of visual feedback for control of nonholonomic vehicles has been reported. Some of the examples are docking and parallel parking maneuvers of cars or vision-based stabilization of a mobile manipulator to a desired pose with respect to a target of interest. Still, many of the approaches are mostly interested in the control part of visual servoing loop considering very simple vision algorithms based on artificial markers. In this paper, we present an approach for nonholonomic visual servoing based on epipolar geometry. The method facilitates a classical teach-by-showing approach where a reference image is used to define the desired pose (position and orientation) of the robot. The major contribution of the paper is the design of the control law that considers nonholonomic constraints of the robot as well as the robust feature detection and matching process based on scale and rotation invariant image features. An extensive experimental evaluation has been performed in a realistic indoor setting and the results are summarized in the paper.

  • 410. Maas, R.
    et al.
    Thippur, Akshaya
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Sehr, A.
    Kellermann, W.
    An uncertainty decoding approach to noise- and reverberation-robust speech recognition2013Ingår i: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, s. 7388-7392Konferensbidrag (Refereegranskat)
    Abstract [en]

    The generic REMOS (REverberation MOdeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hiddenMarkov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios.

  • 411.
    Maboudi Afkham, Heydar
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Animal Recognition Using Joint Visual Vocabulary2009Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    This thesis presents a series of experiments on recognizing animals in complex

    scenes. Unlike usual objects used for the recognition task (cars, airplanes, ...)

    animals appear in a variety of poses and shapes in outdoor images. To perform

    this task a dataset of outdoor images should be provided. Among the available

    datasets there are some animal classes but as discussed in this thesis these

    datasets do not capture the necessary variations needed for realistic analysis.

    To overcome this problem a new extensive dataset,

    KTH-animals

    , containing

    realistic images of animals in complex natural environments. The methods

    designed on the other datasets do not preform well on the animals dataset

    due to the larger variations in this dataset. One of the methods that showed

    promising results on one of these datasets on the animals dataset was applied

    on

    KTH-animals

    and showed how it failed to encode the large variations in

    this dataset.

    To familiarize the reader with the concept of computer vision and the

    mathematics backgrounds a chapter of this thesis is dedicated to this matter.

    This section presents a brief review of the texture descriptors and several

    classification methods together with mathematical and statistical algorithms

    needed by them.

    To analyze the images of the dataset two different methodologies are introduced

    in this thesis. In the first methodology

    fuzzy classifiers

    we analyze

    the images solely based on the animals skin texture of the animals. To do so an

    accurate manual segmentation of the images is provided. Here the skin texture

    is judged using many different features and the results are combined with each

    other with

    fuzzy classifiers

    . Since the assumption of neglecting the background

    information in unrealistic the joint visual vocabularies are introduced.

    Joint visual vocabularies

    is a method for visual object categorization based

    on encoding the joint textural information in objects and the surrounding background,

    and requiring no segmentation during recognition. The framework can

    be used together with various learning techniques and model representations.

    Here we use this framework with simple probabilistic models and more complex

    representations obtained using Support Vector Machines. We prove that

    our approach provides good recognition performance for complex problems

    for which some of the existing methods have difficulties.

    The achievements of this thesis are a challenging database for animal

    recognition. A review of the previous work and related mathematical background.

    Texture feature evaluation on the "KTH-animal" dataset. Introduction

    a method for object recognition based on joint statistics over the image.

    Applying

    different model representation of different complexity within the same

    classification framework, simple probabilistic models and more complex ones

    based on Support Vector Machines.

  • 412.
    Maboudi Afkham, Heydar
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Improving Image Classification Performance using Joint Feature Selection2014Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    In this thesis, we focus on the problem of image classification and investigate how its performance can be systematically improved. Improving the performance of different computer vision methods has been the subject of many studies. While different studies take different approaches to achieve this improvement, in this thesis we address this problem by investigating the relevance of the statistics collected from the image.

    We propose a framework for gradually improving the quality of an already existing image descriptor. In our studies, we employ a descriptor which is composed the response of a series of discriminative components for summarizing each image. As we will show, this descriptor has an ideal form in which all categories become linearly separable. While, reaching this form is not possible, we will argue how by replacing a small fraction of these components, it is possible to obtain a descriptor which is, on average, closer to this ideal form. To do so, we initially identify which components do not contribute to the quality of the descriptor and replace them with more robust components. As we will show, this replacement has a positive effect on the quality of the descriptor.

    While there are many ways of obtaining more robust components, we introduce a joint feature selection problem to obtain image features that retains class discriminative properties while simultaneously generalising between within class variations. Our approach is based on the concept of a joint feature where several small features are combined in a spatial structure. The proposed framework automatically learns the structure of the joint constellations in a class dependent manner improving the generalisation and discrimination capabilities of the local descriptor while still retaining a low-dimensional representations.

    The joint feature selection problem discussed in this thesis belongs to a specific class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization. Here, we focus on the learning of such models by introducing a topological framework and show how it is possible to both reduce the learning complexity and produce more robust decision boundaries. We will also argue how our framework can be used for producing robust decision boundaries without exploiting the dataset bias or relying on accurate annotations.

    To examine the hypothesis of this thesis, we evaluate different parts of our framework on several challenging datasets and demonstrate how our framework is capable of gradually improving the performance of image classification by collecting more robust statistics from the image and improving the quality of the descriptor.

  • 413.
    Maboudi Afkham, Heydar
    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.
    Sullivan, Josephine
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Improving feature level likelihoods using cloud features2012Ingår i: ICPRAM - Proc. Int. Conf. Pattern Recogn. Appl. Methods, 2012, s. 431-437Konferensbidrag (Refereegranskat)
    Abstract [en]

    The performance of many computer vision methods depends on the quality of the local features extracted from the images. For most methods the local features are extracted independently of the task and they remain constant through the whole process. To make features more dynamic and give models a choice in the features they can use, this work introduces a set of intermediate features referred as cloud features. These features take advantage of part-based models at the feature level by combining each extracted local feature with its close by local feature creating a cloud of different representations for each local features. These representations capture the local variations around the local feature. At classification time, the best possible representation is pulled out of the cloud and used in the calculations. This selection is done based on several latent variables encoded within the cloud features. The goal of this paper is to test how the cloud features can improve the feature level likelihoods. The focus of the experiments of this paper is on feature level inference and showing how replacing single features with equivalent cloud features improves the likelihoods obtained from them. The experiments of this paper are conducted on several classes of MSRCv1 dataset.

  • 414.
    Maboudi Afkham, Heydar
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Carlsson, Stefan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Qualitative vocabulary based descriptor2013Ingår i: ICPRAM 2013: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 2013, s. 188-193Konferensbidrag (Refereegranskat)
    Abstract [en]

    Creating a single feature descriptors from a collection of feature responses is an often occurring task. As such the bag-of-words descriptors have been very successful and applied to data from a large range of different domains. Central to this approach is making an association of features to words. In this paper we present a new and novel approach to feature to word association problem. The proposed method creates a more robust representation when data is noisy and requires less words compared to the traditional methods while retaining similar performance. We experimentally evaluate the method on a challenging image classification data-set and show significant improvement to the state of the art.

  • 415.
    Maboudi Afkham, Heydar
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Tavakoli Targhi, Alireza
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Eklundh, Jan-Olof
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Pronobis, Andrzej
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Joint Visual Vocabulary For Animal Classification2008Ingår i: 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, s. 2019-2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a method for visual object categorization based on encoding the joint textural information in objects and the surrounding back-ground, and requiring no segmentation during recognition. The framework can be used together with various learning techniques and model representations. Here we use this framework with simple probabilistic models and more complex representations obtained using Support Vector Machines. We prove that our approach provides good recognition performance for complex problems for which some of the existing methods have difficulties. Additionally, we introduce a new extensive database containing realistic images of animals in complex natural environments. We asses the database in a set of experiments in which we compare the performance of our approach with a recently proposed method.

  • 416.
    Madry, Marianna
    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.
    Detry, Renaud
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Hang, Kaiyu
    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.
    Improving Generalization for 3D Object Categorization with Global Structure Histograms2012Ingår i: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE conference proceedings, 2012, s. 1379-1386Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a new object descriptor for three dimensional data named the Global Structure Histogram (GSH). The GSH encodes the structure of a local feature response on a coarse global scale, providing a beneficial trade-off between generalization and discrimination. Encoding the structural characteristics of an object allows us to retain low local variations while keeping the benefit of global representativeness. In an extensive experimental evaluation, we applied the framework to category-based object classification in realistic scenarios. We show results obtained by combining the GSH with several different local shape representations, and we demonstrate significant improvements to other state-of-the-art global descriptors.

  • 417.
    Madry, Marianna
    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.
    Maboudi Afkham, Heydar
    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.
    Carlsson, Stefan
    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.
    Extracting essential local object characteristics for 3D object categorization2013Ingår i: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2013, s. 2240-2247Konferensbidrag (Refereegranskat)
    Abstract [en]

    Most object classes share a considerable amount of local appearance and often only a small number of features are discriminative. The traditional approach to represent an object is based on a summarization of the local characteristics by counting the number of feature occurrences. In this paper we propose the use of a recently developed technique for summarizations that, rather than looking into the quantity of features, encodes their quality to learn a description of an object. Our approach is based on extracting and aggregating only the essential characteristics of an object class for a task. We show how the proposed method significantly improves on previous work in 3D object categorization. We discuss the benefits of the method in other scenarios such as robot grasping. We provide extensive quantitative and qualitative experiments comparing our approach to the state of the art to justify the described approach.

  • 418.
    Madry, Marianna
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Song, Dan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    "Robot, bring me something to drink from": object representation for transferring task specific grasps2013Ingår i: In IEEE International Conference on Robotics and Automation (ICRA 2012), Workshop on Semantic Perception, Mapping and Exploration (SPME),  St. Paul, MN, USA, May 13, 2012, 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present an approach for taskspecificobject representation which facilitates transfer of graspknowledge from a known object to a novel one. Our representation encompasses: (a) several visual object properties,(b) object functionality and (c) task constrains in order to provide a suitable goal-directed grasp. We compare various features describing complementary object attributes to evaluate the balance between the discrimination and generalization properties of the representation. The experimental setup is a scene containing multiple objects. Individual object hypotheses are first detected, categorized and then used as the input to a grasp reasoning system that encodes the task information. Our approach not only allows to find objects in a real world scene that afford a desired task, but also to generate and successfully transfer task-based grasp within and across object categories.

  • 419.
    Madry, Marianna
    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.
    Song, Dan
    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.
    From object categories to grasp transfer using probabilistic reasoning2012Ingår i: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, s. 1716-1723Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we address the problem of grasp generation and grasp transfer between objects using categorical knowledge. The system is built upon an i) active scene segmentation module, able of generating object hypotheses and segmenting them from the background in real time, ii) object categorization system using integration of 2D and 3D cues, and iii) probabilistic grasp reasoning system. Individual object hypotheses are first generated, categorized and then used as the input to a grasp generation and transfer system that encodes task, object and action properties. The experimental evaluation compares individual 2D and 3D categorization approaches with the integrated system, and it demonstrates the usefulness of the categorization in task-based grasping and grasp transfer.

  • 420.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Bretzner, Lars
    Eklundh, Jan-Olof
    Local Fourir Phase and Disparity Estimates: An Analytical Study1995Ingår i: International Conference on Computer Analysis of Images and Patterns, 1995, Vol. 970, s. 868-873Konferensbidrag (Refereegranskat)
  • 421.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Nordlund, Peter
    Eklundh, Jan-Olof
    A computational model of depth-based attention1996Ingår i: International Conference on Pattern Recognition, 1996, s. 734-739Konferensbidrag (Refereegranskat)
  • 422.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Nordlund, Peter
    Eklundh, Jan-Olof
    Attentional Scene Segmentation: Integrating Depth and Motion2000Ingår i: Computer Vision and Image Understanding, Vol. 78, nr 3, s. 351-373Artikel i tidskrift (Refereegranskat)
  • 423.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Uhlin, Tomas
    Disparity Selection in Binocular Pursuit1995Ingår i: IEICE transactions on information and systems, ISSN 0916-8532, E-ISSN 1745-1361, Vol. E78-D, nr 12, s. 1591-1597Artikel i tidskrift (Refereegranskat)
  • 424.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Uhlin, Tomas
    Eklundh, Jan-Olof
    A Direct Disparity Estimation Technique for Depth Segmentation1996Ingår i: IAPR Workshop on Machine Vision Applications, 1996, s. 530-533Konferensbidrag (Refereegranskat)
  • 425.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Uhlin, Tomas
    Eklundh, Jan-Olof
    Disparity Selection in Binocular Pursuit1994Ingår i: IAPR Workshop on Machine Vision Applications, 1994, s. 182-185Konferensbidrag (Refereegranskat)
  • 426.
    Maki, Atsuto
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Uhlin, Tomas
    Eklundh, Jan-Olof
    Phase-Based Disparity Estimation in Binocular Tracking1993Ingår i: Scandinavian Conference on Image Analysis, 1993Konferensbidrag (Refereegranskat)
  • 427.
    Markdahl, Johan
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Hu, Xiaoming
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Karayiannidis, Yiannis
    A Hybrid Control Approach to Task-Priority Based Mobile ManipulationIngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523Artikel i tidskrift (Refereegranskat)
  • 428.
    Markdahl, Johan
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Karayiannidis, Yiannis
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Hu, Xiaoming
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Cooperative object path following control by means of mobile manipulators: A switched systems approach2012Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline): Robot Control, Vol 10, Part 1, IFAC Papers Online, 2012, s. 773-778Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a switched control algorithm for distributed cooperative manipulation of rigid bodies in a planar setting. More specifically, we consider the problem of making a grasped object follow a desired position and orientation path. The system contains N robots, possibly of heterogeneous designs, each of which consists of a manipulator arm and a nonholonomic mobile platform. Control is based on local information, is carried out on a kinematic level and partly utilizes inverse kinematics. We use Lyapunov-like arguments to prove that the algorithm is almost globally stable and also show that its parameters can be chosen so that any input saturation level is met. The time between certain switches is proved to be bounded below, and the system is shown to be free of any Zeno behavior.

  • 429.
    Markdahl, Johan
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Karayiannidis, Yiannis
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Hu, Xiaoming
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Distributed Cooperative Object Attitude Manipulation2012Ingår i: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, s. 2960-2965Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a local information based control law in order to solve the planar manipulation problem of rotating a grasped rigid object to a desired orientation using multiple mobile manipulators. We adopt a multi-agent systems theory approach and assume that: (i) the manipulators (agents) are capable of sensing the relative position to their neighbors at discrete time instances, (ii) neighboring agents may exchange information at discrete time instances, and (iii) the communication topology is connected. Control of the manipulators is carried out at a kinematic level in continuous time and utilizes inverse kinematics. The mobile platforms are assigned trajectory tracking tasks that adjust the positions of the manipulator bases in order to avoid singular arm configurations. Our main result concerns the stability of the proposed control law.

  • 430.
    Martinez, David
    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.
    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.
    Modeling and recognition of actions through motor primitives2008Ingår i: 2008 IEEE International Conference On Robotics And Automation: Vols 1-9, 2008, s. 1704-1709Konferensbidrag (Refereegranskat)
    Abstract [en]

    We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination of discriminative (support vector machines, conditional random fields) and generative approaches (hidden Markov models). We examine the hypothesis that complex actions can be represented as a sequence of motion or action primitives. The experimental evaluation, performed with five object manipulation actions and 10 people, investigates the modeling approach of the primitive action structure and compares the performance of the considered generative and discriminative models.

  • 431.
    Marzinotto, Alejandro
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Colledanchise, Michele
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ögren, Petter
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Towards a Unified Behavior Trees Framework for Robot Control2014Ingår i: Robotics and Automation (ICRA), 2014 IEEE International Conference on , IEEE Robotics and Automation Society, 2014, s. 5420-5427Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a unified framework for Behavior Trees (BTs), a plan representation and execution tool. The available literature lacks the consistency and mathematical rigor required for robotic and control applications. Therefore, we approach this problem in two steps: first, reviewing the most popular BT literature exposing the aforementioned issues; second, describing our unified BT framework along with equivalence notions between BTs and Controlled Hybrid Dynamical Systems (CHDSs). This paper improves on the existing state of the art as it describes BTs in a more accurate and compact way, while providing insight about their actual representation capabilities. Lastly, we demonstrate the applicability of our framework to real systems scheduling open-loop actions in a grasping mission that involves a NAO robot and our BT library.

  • 432.
    Marzinotto, Alejandro
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Rope through Loop Insertion for Robotic Knotting: A Virtual Magnetic Field Formulation2016Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Inserting an end of a rope through a loop is a common and important action that is required for creating most types of knots. To perform this action, we need to pass the end of the rope through an area that is enclosed by another segment of rope. As for all knotting actions, the robot must for this exercise control over a semi-compliant and flexible body whose complex 3d shape is difficult to perceive and follow. Additionally, the target loop often deforms during the insertion. We address this problem by defining a virtual magnetic field through the loop's interior and use the Biot Savart law to guide the robotic manipulator that holds the end of the rope. This approach directly defines, for any manipulator position, a motion vector that results in a path that passes through the loop. The motion vector is directly derived from the position of the loop and changes as soon as it moves or deforms. In simulation, we test the insertion action against dynamic loop deformation of different intensity. We also combine insertion with grasp and release actions, coordinated by a hybrid control system, to tie knots in simulation and with a NAO robot.

  • 433.
    Marzinotto, Alejandro
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Stork, Johannes A.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Dimarogonas, Dino V.
    KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.
    Kragic Jensfelt, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Cooperative grasping through topological object representation2015Ingår i: IEEE-RAS International Conference on Humanoid Robots, IEEE Computer Society, 2015, s. 685-692Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a cooperative grasping approach based on a topological representation of objects. Using point cloud data we extract loops on objects suitable for generating entanglement. We use the Gauss Linking Integral to derive controllers for multi-agent systems that generate hooking grasps on such loops while minimizing the entanglement between robots. The approach copes well with noisy point cloud data, it is computationally simple and robust. We demonstrate the method for performing object grasping and transportation, through a hooking maneuver, with two coordinated NAO robots.

  • 434. Miao, Li
    et al.
    Bekiroglu, Yasemin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Billard, Aude
    Learning of Grasp Adaptation through Experience and Tactile Sensing2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

  • 435.
    Mitsunaga, Noriaki
    et al.
    Osaka Kyoiku University.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kanda, Takayuki
    Advanced Telecommunications Research International.
    Ishiguro, Hiroshi
    Osaka University.
    Hagita, Norihiro
    Advanced Telecommunications Research International.
    Adapting Nonverbal Behavior Parameters to be Preferred by Individuals2012Ingår i: Human-Robot Interaction in Social Robotics / [ed] Takayuki Kanda and Hiroshi Ishiguro, Boca Raton, FL, USA: CRC Press, 2012, 1, s. 312-324Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 436. Mitsunaga, Noriaki
    et al.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kanda, Takayuki
    Ishiguro, Hiroshi
    Hagita, Norihiro
    Adapting robot behavior for human-robot interaction2008Ingår i: IEEE Transactions on Robotics, ISSN 1552-3098, Vol. 24, nr 4, s. 911-916Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human-robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human-robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. However, most previous, research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interactions. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented.

  • 437. Mitsunaga, Noriaki
    et al.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kanda, Takayuki
    Ishiguro, Hiroshi
    Hagita, Norihiro
    Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning2005Ingår i: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005)., IEEE , 2005, s. 1594-1601Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze-meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.

  • 438.
    Mizuyama, Hajime
    et al.
    Department of Mechanical Engineering and Science, Kyoto University.
    Yamada, Kayo
    Department of Mechanical Engineering and Science, Kyoto University.
    Tanaka, Kazuto
    Department of Biomedical Engineering, Doshisha University.
    Maki, Atsuto
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. Graduate School of Informatics, Kyoto University.
    Explanatory analysis of the manner in which an instructor adaptively organizes skilled motion teaching process2013Ingår i: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 43, nr 5, s. 430-438Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Mastering a skilled motion usually requires a step-by-step progression through multiple learning phases with different subgoals. It is not easy for a learner to properly organize such a complex learning process without assistance. Hence, this task is often facilitated interactively by a human instructor through verbal advice. In many cases, the instructor's teaching strategy in relation to decomposing the entire learning process into phases, setting a subgoal for each learning phase, choosing verbal advice to guide the learner toward this subgoal, etc. remains intuitive and has not yet been formally understood. Thus, taking the basic motion of wok handling as an example, this paper presents several concrete teaching processes involving an advice sequence and the corresponding changes in the motion performance in a feature variable space. Thereby, the paper analyzes and represents the actual strategy taken in an easy-to-interpret form. As a result, it confirms that the instructor determines the set of advice elements to be given based, not simply on the observable characteristics of the latest motion performance, but more adaptively upon the interaction history with the learner. Relevance to industry: Teaching a skilled motion efficiently is essential in various industrial sectors such as those involving manual assembly. An experienced instructor may adaptively organize the entire interactive process of teaching a learner to accelerate the learning of correct motion skills.

  • 439. Mozos, O.M.
    et al.
    Triebel, R.
    Jensfelt, Patric
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Rottmann, A.
    Burgard, W.
    Supervised semantic labeling of places using information extracted from sensor data2007Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 55, nr 5, s. 391-402Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction with humans. As an example, natural language terms like ``corridor" or ``room" can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with the relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

  • 440.
    Naderi Parizi, Sobhan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Tavakoli Targhi, Alireza
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Aghazadeh, Omid
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Eklundh, Jan-Olof
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    READING STREET SIGNS USING A GENERIC STRUCTURED OBJECT DETECTION AND SIGNATURE RECOGNITION APPROACH2009Ingår i: VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 2, SETUBAL: INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION , 2009, s. 346-355Konferensbidrag (Refereegranskat)
    Abstract [en]

    In the paper we address the applied problem of detecting and recognizing street name plates in urban images by a generic approach to structural object detection and recognition. A structured object is detected using a boosting approach and false positives are filtered using a specific method called the texture transform. In a second step the subregion containing the key information, here the text, is segmented out. Text is in this case characterized as texture and a texton based technique is applied. Finally the texts are recognized by using Dynamic Time Warping on signatures created from the identified regions. The recognition method is general and only requires text in some form, e.g. a list of printed words, but no image models of the plates for learning. Therefore, it can be shown to scale to rather large data sets. Moreover, due to its generality it applies to other cases, such as logo and sign recognition. On the other hand the critical part of the method lies in the detection step. Here it relied on knowledge about the appearance of street signs. However, the boosting approach also applies to other cases as long as the target region is structured in some way. The particular scenario considered deals with urban navigation and map indexing by mobile users, e.g. when the images are acquired by a mobile phone.

  • 441. Nalpantidis, L.
    et al.
    Kragic Jensfelt, 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.
    Kostavelis, I.
    Gasteratos, A.
    Theta- disparity: An efficient representation of the 3D scene structure2015Ingår i: 13th International Conference on Intelligent Autonomous Systems, IAS 2014, Springer, 2015, Vol. 302, s. 795-806Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a new representation of 3D scene structure, named thetadisparity. The proposed representation is a 2D angular depth histogram that is calculated using a disparity map. It models the structure of the prominent objects in the scene and reveals their radial distribution relative to a point of interest. The proposed representation is analyzed and used as a basic attention mechanism to autonomously resolve two different robotic scenarios. The method is efficient due to the low computational complexity. We show that the method can be successfully used for the planning of different tasks in the industrial and service robotics domains, e.g., object grasping, manipulation, plane extraction, path detection, and obstacle avoidance.

  • 442.
    Nalpantidis, Lazaros
    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.
    Björkman, Mårten
    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.
    YES - YEt another object Segmentation: exploiting camera movement2012Ingår i: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE , 2012, s. 2116-2121Konferensbidrag (Refereegranskat)
    Abstract [en]

    We address the problem of object segmentation in image sequences where no a-priori knowledge of objects is assumed. We take advantage of robots' ability to move, gathering multiple images of the scene. Our approach starts by extracting edges, uses a polar domain representation and performs integration over time based on a simple dilation operation. The proposed system can be used for providing reliable initial segmentation of unknown objects in scenes of varying complexity, allowing for recognition, categorization or physical interaction with the objects. The experimental evaluation on both self-captured and a publicly available dataset shows the efficiency and stability of the proposed method.

  • 443.
    Nalpantidis, Lazaros
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Gasteratos, A.
    Stereo vision depth estimation methods for robotic applications2013Ingår i: Robotics: Concepts, Methodologies, Tools, and Applications, IGI Global, 2013, Vol. 3, s. 1461-1481Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for visionbased autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

  • 444.
    Nalpantidis, Lazaros
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Stereo vision depth estimation methods for robotic applications2011Ingår i: Depth Map and 3D Imaging Applications: Algorithms and Technologies / [ed] A. S. Malik, T.-S. Choi, and H. Nisar, IGI Global, 2011, s. 397-417Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for vision-based autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

  • 445.
    Nalpantidis, Lazaros
    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.
    Kostavelis, I.
    Gasteratos, A.
    Intelligent stereo vision in autonomous robot traversability estimation2012Ingår i: Robotic Vision: Technologies for Machine Learning and Vision Applications, IGI Global, 2012, s. 193-209Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Traversability estimation is the process of assessing whether a robot is able to move across a specific area. Autonomous robots need to have such an ability to automatically detect and avoid non-traversable areas and, thus, stereo vision is commonly used towards this end constituting a reliable solution under a variety of circumstances. This chapter discusses two different intelligent approaches to assess the traversability of the terrain in front of a stereo vision-equipped robot. First, an approach based on a fuzzy inference system is examined and then another approach is considered, which extracts geometrical descriptions of the scene depth distribution and uses a trained support vector machine (SVM) to assess the traversability. The two methods are presented and discussed in detail.

  • 446.
    Nalpantidis, Lazaros
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kostavelis, I.
    Gasteratos, A.
    Intelligent stereo vision in autonomous robot traversability estimation2013Ingår i: Robotics: Concepts, Methodologies, Tools, and Applications, IGI Global, 2013, Vol. 1, s. 350-365Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Traversability estimation is the process of assessing whether a robot is able to move across a specific area. Autonomous robots need to have such an ability to automatically detect and avoid non-traversable areas and, thus, stereo vision is commonly used towards this end constituting a reliable solution under a variety of circumstances. This chapter discusses two different intelligent approaches to assess the traversability of the terrain in front of a stereo vision-equipped robot. First, an approach based on a fuzzy inference system is examined and then another approach is considered, which extracts geometrical descriptions of the scene depth distribution and uses a trained support vector machine (SVM) to assess the traversability. The two methods are presented and discussed in detail.

  • 447.
    Nazem, Ali
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kootstra, Geert
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Interfacing a parallel simulation of a neuronal network to robotic hardware using MUSIC, with application to real-time figure-ground segregation.2011Ingår i: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 12, nr Suppl 1, s. 78-78Artikel i tidskrift (Refereegranskat)
  • 448.
    Nazem, Ali
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kootstra, Geert
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Djurfeldt, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Parallel implementation of a biologically inspired model of figure-ground segregation: Application to real-time data using MUSIC2011Ingår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    MUSIC, the multi-simulation coordinator, supports communication between neuronal-network simulators, or other (parallel) applications, running in a cluster super-computer. Here, we have developed a class library that interfaces between MUSIC-enabled software and applications running on computers outside of the cluster. Specifically, we have used this component to interface the cameras of a robotic head to a neuronal-network simulation running on a Blue Gene/L supercomputer. Additionally, we have developed a parallel implementation of a model for figure ground segregation based on neuronal activity in the Macaque visual cortex. The interface enables the figure ground segregation application to receive real-world images in real-time from the robot. Moreover, it enables the robot to be controlled by the neuronal network.

  • 449.
    Nillius, Peter
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Fysik, Medicinsk avbildning.
    Sullivan, Josephine
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Argyros, Antonis
    Shading models for illumination and reflectance invariant shape detectors2008Ingår i: 2008 IEEE Conference On Computer Vision And Pattern Recognition: Vols 1-12, 2008, s. 3353-3360Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many objects have smooth surfaces of a fairly uniform color, thereby exhibiting shading patterns that reveal information about its shape, an important clue to the nature of the object. This papers explores extracting this information from images, by creating shape detectors based on shading. Recent work has derived low-dimensional models of shading that can handle realistic unknown lighting conditions and surface reflectance properties. We extend this theory by also incorporating variations in the surface shape. In doing so it enables the creation of very general models for the 2D appearance of objects, not only coping with variations in illumination and BRDF but also in shape alterations such as small scale and pose changes. Using this framework we propose a scheme to build shading models that can be used for shape detection in a bottom up fashion without any a priori knowledge about the scene. From the developed theory we construct detectors for two basic shape primitives, spheres and cylinders. Their performance is evaluated by extensive synthetic experiments as well as experiments on real images.

  • 450. Norlander, Rickard
    et al.
    Grahn, Josef
    Maki, Atsuto
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Wooden Knot Detection Using ConvNet Transfer Learning2015Ingår i: Scandinavian Conference on Image Analysis, 2015Konferensbidrag (Refereegranskat)
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