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  • 1. Bayro-Corrochano, Eduardo
    et al.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Advances in theory and applications of pattern recognition, image processing and computer vision2011In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 32, no 16, p. 2143-2144Article in journal (Refereed)
  • 2.
    Björkman, Mårten
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Attending, Foveating and Recognizing Objects in Real World Scenes2004In: British Machine Vision Conference (BMVC), London, UK / [ed] Andreas Hoppe, Sarah Barman, Tim Ellis, BMVA Press , 2004, p. 227-236Conference paper (Refereed)
    Abstract [en]

    Recognition in cluttered real world scenes is a challenging problem. To find a particular object of interest within a reasonable time, a wide field of view is preferable. However, as we will show with practical experiments, robust recognition is easier if the object is foveated and subtends a considerable partof the visual field. In this paper a binocular system able to overcome these two conflicting requirements will be presented. The system consists of two sets of cameras, a wide field pair and a foveal one. From disparities a number of object hypotheses are generated. An attentional process based on hue and 3D size guides the foveal cameras towards the most salient regions. With the object foveated and segmented in 3D, recognition is performed using scale invariant features. The system is fully automised and runs at real-time speed.

  • 3.
    Björkman, Mårten
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Foveated Figure-Ground Segmentation and Its Role in Recognition2005In: BMVC 2005 - Proceedings of the British Machine Vision Conference 2005 / [ed] William Clocksin, Andrew Fitzgibbon, Philip Torr, British Machine Vision Association, BMVA , 2005, p. 819-828Conference paper (Refereed)
    Abstract [en]

    Figure-ground segmentation and recognition are two interrelated processes. In this paper we present a method for foveated segmentation and evaluate it in the context of a binocular real-time recognition system. Segmentation is solved as a binary labeling problem using priors derived from the results ofa simplistic disparity method. Doing so we are able to cope with situations when the disparity range is very wide, situations that has rarely been considered, but appear frequently for narrow-field camera sets. Segmentation and recognition are then integrated into a system able to locate, attend to and recognise objects in typical cluttered indoor scenes. Finally, we try to answer two questions: is recognition really helped by segmentation and what is the benefit of multiple cues for recognition?

  • 4.
    Björkman, Mårten
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Real-time epipolar geometry estimation of binocular stereo heads2002In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 24, no 3, p. 425-432Article in journal (Refereed)
    Abstract [en]

    Stereo is an important cue for visually guided robots. While moving around in the world, such a robot can use dynamic fixation to overcome limitations in image resolution and field of view. In this paper, a binocular stereo system capable of dynamic fixation is presented. The external calibration is performed continuously taking temporal consistency into consideration, greatly simplifying the process. The essential matrix, which is estimated in real-time, is used to describe the epipolar geometry. It will be shown, how outliers can be identified and excluded from the calculations. An iterative approach based on a differential model of the optical flow, commonly used in structure from motion, is also presented and tested towards the essential matrix. The iterative method will be shown to be superior in terms of both computational speed and robustness, when the vergence angles are less than about 15degrees. For larger angles, the differential model is insufficient and the essential matrix is preferably used instead.

  • 5.
    Björkman, Mårten
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Vision in the real world: Finding, attending and recognizing objects2006In: International journal of imaging systems and technology (Print), ISSN 0899-9457, E-ISSN 1098-1098, Vol. 16, no 5, p. 189-208Article in journal (Refereed)
    Abstract [en]

    In this paper we discuss the notion of a seeing system that uses vision to interact with its environment. The requirements on such a system depend on the tasks it is involved in and should be evaluated with these in mind. Here we consider the task of finding and recognizing objects in the real world. After a discussion of the needed functionalities and issues about the design we present an integrated real-time vision system capable of finding, attending and recognizing objects in real settings. The system is based on a dual set of cameras, a wide field set for attention and a foveal one for recognition. The continuously running attentional process uses top-down object characteristics in terms of hue and 3D size. Recognition is performed with objects of interest foveated and segmented from its background. We describe the system structure as well as the different components in detail and present experimental evaluations of its overall performance.

  • 6. Bray, Matthieu
    et al.
    Sidenbladh, Hedvig
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Recognition of gestures in the context of speech2002In: 16th International Conference on Pattern Recognition, 2002. Proceedings., 2002Conference paper (Refereed)
    Abstract [en]

    The scope of this paper is the interpretation of a user's intention via a video camera and a speech recognizer In comparison to previous work which only takes into account gesture recognition, we demonstrate that by including speech, system comprehension increases. For the gesture recognition, the user must wear a colored glove, then we extract the velocity of the center of gravity of the hand. A Hidden Markov Model (HMM) is learned for each gesture that we want to recognize. In a dynamic action, to know if a gesture has been performed or not, we implement a threshold model below which the gesture is not detected. The off line tests for gesture recognition have a success rate exceeding 85% for each gesture. The combination of speech and gestures is realized using Bayesian theory.

  • 7.
    Brunnström, Kjell
    et al.
    KTH, Superseded Departments (pre-2005), Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, Superseded Departments (pre-2005), Numerical Analysis and Computer Science, NADA.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    On Scale and Resolution in the Analysis of Local Image Structure1990In: Proc. 1st European Conf. on Computer Vision, 1990, Vol. 427, p. 3-12Conference paper (Refereed)
    Abstract [en]

    Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans.

    In this paper we will investigate problems in connection with foveation, that is examining selected regions of the world at high resolution. We will especially consider the problem of finding and classifying junctions from this aspect. We will show that foveation as simulated by controlled, active zooming in conjunction with scale-space techniques allows robust detection and classification of junctions.

  • 8.
    Brunnström, Kjell
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Scale and Resolution in Active Analysis of Local Image Structure1990In: Image and Vision Computing, Vol. 8, p. 289-296Article in journal (Refereed)
    Abstract [en]

    Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans. Problems connected to foveation (examination of selected regions of the world at high resolution) are examined. In particular, the problem of finding and classifying junctions from this aspect is considered. It is shown that foveation as simulated by controlled, active zooming in conjunction with scale-space techniques allows for robust detection and classification of junctions.

  • 9.
    Brunnström, Kjell
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lindeberg, Tony
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Active detection and classification of junctions by foveation with a head-eye system guided by the scale-space primal sketch1992In: Computer Vision — ECCV'92: Second European Conference on Computer Vision Santa Margherita Ligure, Italy, May 19–22, 1992 Proceedings / [ed] Guilo Sandini, Springer Berlin/Heidelberg, 1992, p. 701-709Conference paper (Refereed)
    Abstract [en]

    We consider how junction detection and classification can be performed in an active visual system. This is to exemplify that feature detection and classification in general can be done by both simple and robust methods, if the vision system is allowed to look at the world rather than at prerecorded images. We address issues on how to attract the attention to salient local image structures, as well as on how to characterize those.

  • 10. Caputo, Barbara
    et al.
    Hayman, Eric
    Fritz, Mario
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Classifying materials in the real world2010In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 28, no 1, p. 150-163Article in journal (Refereed)
    Abstract [en]

    Classifying materials from their appearance is challenging. Impressive results have been obtained under varying illumination and pose conditions. Still, the effect of scale variations and the possibility to generalise across different material samples are still largely unexplored. This paper (A preliminary version of this work was presented in Hayman et al. [E. Hayman, B. Caputo, M.J. Fritz, J.-O. Eklundh, On the significance of real world conditions for material classification, in: Proceedings of the ECCV, Lecture Notes in Computer Science, vol. 4, Springer, Prague, 2004, pp. 253-266].) addresses these issues, proposing a pure learning approach based on support vector machines. We study the effect of scale variations first on the artificially scaled CUReT database, showing how performance depends on the amount of scale information available during training. Since the CUReT database contains little scale variation and only one sample per material, we introduce a new database containing 10 CUReT materials at different distances, pose and illumination. This database provides scale variations, while allowing to evaluate generalisation capabilities: does training on the CUReT database enable recognition of another piece of sandpaper? Our results demonstrate that this is not yet possible, and that material classification is far from being solved in scenarios of practical interest.

  • 11.
    Cornelius, Hugo
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Object and pose recognition using contour and shape information2005In: 2005 12th International Conference on Advanced Robotics, NEW YORK, NY: IEEE , 2005, p. 613-620Conference paper (Refereed)
    Abstract [en]

    Object recognition and pose estimation are of significant importance for robotic visual servoing, manipulation and grasping tasks. Traditionally, contour and shape based methods have been considered as most adequate for estimating stable and feasible grasps, [1]. More recently, a new research direction has been advocated in visual servoing where image moments are used to define a suitable error function to be minimized. Compared to appearance based methods, contour and shape based approaches are also suitable for use with range sensors such as, for example, lasers. In this paper, we evaluate a contour based object recognition system building on the method in [2], suitable for objects of uniform color properties such as cups, cutlery, fruits etc. This system is one of the building blocks of a more complex object recognition system based both on stereo and appearance cues, [3]. The system has a significant potential both in terms of service robot and programming by demonstration tasks. Experimental evaluation shows promising results in terms of robustness to occlusion and noise.

  • 12.
    Eklundh, Jan-Olof
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Björkman, Mårten
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Recognition of Objects in the Real World from a Systems Perspective2005In: Kuenstliche Intelligenz, ISSN 0933-1875, Vol. 19, no 2, p. 12-17Article in journal (Refereed)
    Abstract [en]

    Based on a discussion of the requirements for a vision system operating in the real world we present a real-time system that includes a set of behaviours that makes it capable of handling a series of typical tasks. The system is able to localise objects of interests based on multiple cues, attend to the objects and finally recognise them while they are in fixation. A particular aspect of the system concerns the use of 3D cues. We end by showing the system running in practice and present results highlighting the merits of 3D-based attention and segmentation and multiple cues for recognition.

  • 13.
    Hayman, Eric
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Fritz, Mario
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    On the significance of real-world conditions for material classification2004In: COMPUTER VISION - ECCV 2004, PT 4, BERLIN: SPRINGER , 2004, Vol. 2034, p. 253-266Conference paper (Refereed)
    Abstract [en]

    Classifying materials from their appearance is a challenging problem, highlights especially if illumination and pose conditions are permitted to change: and shadows caused by 3D structure can radically alter a sample's visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many images of 61 materials under different conditions. A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database. In our work we additionally investigate the effect of scale since robustness to viewing distance and zoom settings is crucial in many real-world situations. Indeed, a material's appearance can vary considerably as fine-level detail becomes visible or disappears as the camera moves towards or away from the subject. We handle scale- variations using a pure-learning approach, incorporating samples imaged at different distances into the training set. An empirical investigation is conducted to show how the classification accuracy decreases as less scale information is made available during training. Since the CUReT database contains little scale variation, we introduce a new database which images ten CUReT materials at different distances, while also maintaining some change in pose and illumination. The first aim of the database is thus to provide scale variations, but a second and equally important objective is to attempt to recognise different samples of the CUReT materials. For instance, does training on the CUReT database enable recognition of anotherpiece of sandpaper? The results clearly demonstrate that it is not possible to do so with any acceptable degree of accuracy. Thus we conclude that impressive results even on a well-designed database such as CUReT, does not imply that material classification is close to being a solved problem under real-world conditions.

  • 14.
    Kragic, Danica
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Björkman, Mårten
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Christensen, Henrik I.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Issues and Strategies for Robotic Object Manipulation in Domestic Settings2004Conference paper (Other academic)
    Abstract [en]

    Many robotic tasks such as autonomous navigation,human-machine collaboration, object manipulationand grasping facilitate visual information. Some of themajor reasearch and system design issues in terms of visualsystems are robustness and flexibility.In this paper, we present a number of visual strategiesfor robotic object manipulation tasks in natural, domesticenvironments. Given a complex fetch-and-carry type oftasks, the issues related to the whole detect-approachgrasploop are considered. Our vision system integratesa number of algorithms using monocular and binocularcues to achieve robustness in realistic settings. The cuesare considered and used in connection to both foveal andperipheral vision to provide depth information, segmentthe object(s) of interest in the scene, object recognition,tracking and pose estimation. One important propertyof the system is that the step from object recognitionto pose estimation is completely automatic combiningboth appearance and geometric models. Rather thanconcentrating on the integration issues, our primary goalis to investigate the importance and effect of cameraconfiguration, their number and type, to the choice anddesign of the underlying visual algorithms. Experimentalevaluation is performed in a realistic indoor environmentwith occlusions, clutter, changing lighting and backgroundconditions.

  • 15.
    Kragic, Danica
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Björkman, Mårten
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Christensen, Henrik I.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Vision for robotic object manipulation in domestic settings2005In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 52, no 1, p. 85-100Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a vision system for robotic object manipulation tasks in natural, domestic environments. Given complex fetch-and-carry robot tasks, the issues related to the whole detect-approach-grasp loop are considered. Our vision system integrates a number of algorithms using monocular and binocular cues to achieve robustness in realistic settings. The cues are considered and used in connection to both foveal and peripheral vision to provide depth information, segmentation of the object(s) of interest, object recognition, tracking and pose estimation. One important property of the system is that the step from object recognition to pose estimation is completely automatic combining both appearance and geometric models. Experimental evaluation is performed in a realistic indoor environment with occlusions, clutter, changing lighting and background conditions.

  • 16. Kruger, Norbert
    et al.
    Piater, Justus
    Worgotter, Florentin
    Geib, Christopher
    Petrick, Ron
    Steedman, Mark
    Asfour, Tamim
    Kraft, Dirk
    Hommel, Bernhard
    Agostini, Alejandro
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    Kruger, Volker
    Torras, Carme
    Dillmann, Rudiger
    A Formal Definition of Object-Action Complexes and Examples at Different Levels of the Processing Hierarchy2009In: Computer and Information Science, ISSN 1913-8989, E-ISSN 1913-8997, p. 1-39Article in journal (Refereed)
    Abstract [en]

    In this report the authors define and describe the concept of Object-Action Complexes and give some examples. OACs combine the concept of affordance with the computational efficiency of STRIPS. Affordance is the relation between a situation and the action that it allows. OACs are proposed as a framework for representing actions, objects and the learning process that constructs such representations at all levels. Formally, an OAC is defined as a triplet, composed of a unique ID, a predition function that codes the systems belief on how the world (which is defined as a kind of global attribute space) will change after applying the OAC and a statisical measure representing the success of an OAC. The prediction function is thereby a mapping within the global attribute space. The measurement captures the accuracy of this prediction function and describes the reliability of the OAC. Therefore, it can be used for optimal decision making, predicion of the outcome of a certain action and learning.

  • 17.
    Kyrki, Ville
    et al.
    Lappeenranta University of Technology, Finland.
    Serrano Vicente, Isabel
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Action Recognition and Understanding using Motor Primitives2007In: 2007 RO-MAN: 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, 2007, p. 1113-1118Conference paper (Refereed)
    Abstract [en]

    We investigate modeling and recognition of arm manipulation actions of different levels of complexity. To model the process, we are using a combination of discriminative support vector machines and generative hidden Markov models. The experimental evaluation, performed with 10 people, investigates both definition and structure of primitive motions as well as the validity of the modeling approach taken.

  • 18.
    Lindeberg, Tony
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Construction of a Scale-Space Primal Sketch1990In: Proceedings of the British Machine Vision Conference 1990: BMVC'90 (Oxford, England), The British Machine Vision Association and Society for Pattern Recognition , 1990, p. 97-102Conference paper (Refereed)
    Abstract [en]

    We present a multi-scale representation of grey-level shape, called scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure --- stable scales and regions of interest --- in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives perceptually intuitive results.

  • 19.
    Loy, Gareth
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Detecting symmetry and symmetric constellations of features2006In: COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS / [ed] Leonardis, A; Bischof, H; Pinz, A, 2006, Vol. 3952, p. 508-521Conference paper (Refereed)
    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.

  • 20.
    Maboudi Afkham, Heydar
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Tavakoli Targhi, Alireza
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Joint Visual Vocabulary For Animal Classification2008In: 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, p. 2019-2022Conference paper (Refereed)
    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.

  • 21. Maki, A.
    et al.
    Nordlund, P.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Attentional scene segmentation: Integrating depth and motion2000In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 78, no 3, p. 351-373Article in journal (Refereed)
    Abstract [en]

    We present an approach to attention in active computer vision. The notion of attention plays an important role in biological vision. In recent years, and especially with the emerging interest in active vision, computer vision researchers have been increasingly concerned with attentional mechanisms as well. The basic principles behind these efforts are greatly influenced by psychophysical research. That is the case also in the work presented here, which adapts to the model of Treisman (1985, Comput. Vision Graphics Image Process. Image Understanding 31., 156-177), with an early parallel stage with preattentive cues followed by a later serial stage where the cues are integrated. The contributions in our approach are (i) the incorporation of depth information from stereopsis, (ii) the simple implementation of low level modules such as disparity and flow by local phase, and (iii) the cue integration along pursuit and saccade mode that allows us a proper target selection based on nearness and motion. We demonstrate the technique by experiments in which a moving observer selectively masks out different moving objects in real scenes.

  • 22.
    Naderi Parizi, Sobhan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Tavakoli Targhi, Alireza
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Aghazadeh, Omid
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    READING STREET SIGNS USING A GENERIC STRUCTURED OBJECT DETECTION AND SIGNATURE RECOGNITION APPROACH2009In: 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, p. 346-355Conference paper (Refereed)
    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.

  • 23. Nalpantidis, L.
    et al.
    Krüger, V.
    Eklundh, Jan-Olof
    Gasteratos, A.
    Computer vision systems: 10th International conference, ICVS 2015 Copenhagen, Denmark, july 6–9, 2015 proceedings2015In: 10th International Conference on Computer Vision Systems, ICVS 2015, Springer, 2015Conference paper (Refereed)
  • 24.
    Peter, Nillius
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Classifying materials from their reflectance properties2004In: COMPUTER VISION: ECCV 2004, PT 4, BERLIN: SPRINGER , 2004, Vol. 2034, p. 366-376Conference paper (Refereed)
    Abstract [en]

    We explore the possibility of recognizing the surface material from a single image with unknown illumination, given the shape of the surface. Model-based PCA is used to create a low-dimensional basis to represent the images. Variations in the illumination create manifolds in the space spanned by this basis. These manifolds are learnt using captured illumination maps and the CUReT database. Classification of the material is done by finding the manifold closest to the point representing the image of the material. Testing on synthetic data shows that the problem is hard. The materials form groups where the materials in a group often are mis-classifed as one of the other materials in the group. With a grouping algorithm we find a grouping of the materials in the CUReT database. Tests on images of real materials in natural illumination settings show promising results.

  • 25.
    Rasolzadeh, Babak
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Björkman, Mårten
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    An attentional system combining top-down and bottom-up influences2006Conference paper (Refereed)
  • 26.
    Rasolzadeh, Babak
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Targhi, Alireza Tavakoli
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    An attentional system combining top-down and bottom-up influences2007In: Attention In Cognitive Systems: Theories And Systems From An Interdisciplinary Viewpoint / [ed] Paletta, L; Rome, E, 2007, Vol. 4840, p. 123-140Conference paper (Refereed)
    Abstract [en]

    Attention plays an important role in human processing of sensory information as a mean of focusing resources toward the most important inputs at the moment. It has in particular been shown to be a key component of vision. In vision it has been argued that the attentional processes are crucial for dealing with the complexity of real world scenes. The problem has often been posed in terms of visual search tasks. It has been shown that both the use of prior task and context information - top-down influences - and favoring information that stands out clearly in the visual field - bottom-up influences - can make such search more efficient. In a generic scene analysis situation one presumably has a combination of these influences and a computational model for visual attention should therefore contain a mechanism for their integration. Such models are abundant for human vision, but relatively few attempts have been made to define any that apply to computer vision. In this article we describe a model that performs such a combination in a principled way. The system learns an optimal representation of the influences of task and context and thereby constructs a biased saliency map representing the top-down information. This map is combined with bottom-up saliency maps in a process evolving over time as a function over the input. The system is applied to search tasks in single images as well as in real scenes, in the latter case using an active vision system capable of shifting its gaze. The proposed model is shown to have desired qualities and to go beyond earlier proposed systems.

  • 27.
    Targhi, Alireza T.
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Hayman, Eric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Shahshahani, M.
    The Eigen-transforrn and applications2006In: COMPUTER VISION - ACCV 2006, PT I / [ed] Narayanan, PJ; Nayar, SK; Shum, HY, 2006, Vol. 3851, p. 70-79Conference paper (Refereed)
    Abstract [en]

    This paper introduces a novel texture descriptor, the Eigen-transform. The transform provides a measure of roughness by considering the eigenvalues of a matrix which is formed very simply by inserting the greyvalues of a square patch around a pixel directly into a matrix of the same size. The eigenvalue of largest magnitude turns out to give a smoothed version of the original image, but the eigenvalues of smaller magnitude encode high frequency information characteristic of natural textures. A major advantage of the Eigen-transform is that it does not fire on straight, or locally straight, brightness edges, instead it reacts almost entirely to the texture itself. This is in contrast to many other descriptors such as Gabor filters or the standard deviation of greyvalues of the patch. These properties make it remarkably well suited to practical applications. Our experiments focus on two main areas. The first is in bottom-up visual attention where textured objects pop out from the background using the Eigen-transform. The second is unsupervised texture segmentation with particular emphasis on real-world, cluttered indoor environments. We compare results with other state-of-the-art methods and find that the Eigen-transform is highly competitive, despite its simplicity and low dimensionality.

  • 28.
    Targhi, Alireza Tavakoli
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Hayman, Eric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Shahshahani, M.
    The Eigen-transform and applications2006Conference paper (Refereed)
    Abstract [en]

    This paper introduces a novel texture descriptor, the Eigen-transform. The transform provides a measure of roughness by considering the eigenvalues of a matrix which is formed very simply by inserting the greyvalues of a square patch around a pixel directly into a matrix of the same size. The eigenvalue of largest magnitude turns out to give a smoothed version of the original image, but the eigenvalues of smaller magnitude encode high frequency information characteristic of natural textures. A major advantage of the Eigen-transform is that it does not fire on straight, or locally straight, brightness edges, instead it reacts almost entirely to the texture itself. This is in contrast to many other descriptors such as Gabor filters or the standard deviation of greyvalues of the patch. These properties make it remarkably well suited to practical applications. Our experiments focus on two main areas. The first is in bottom-up visual attention where textured objects pop out from the background using the Eigen-transform. The second is unsupervised texture segmentation with particular emphasis on real-world, cluttered indoor environments. We compare results with other state-of-the-art methods and find that the Eigen-transform is highly competitive, despite its simplicity and low dimensionality.

  • 29.
    Tavakoli Targhi, Alireza
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Björkman, Mårten
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Hayman, Eric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Real-time texture detection using the LU-transform2006In: Real-time texture detection using the LU-transform, 2006Conference paper (Refereed)
    Abstract [en]

    This paper introduces a fast texture descriptor, the LU-transform. Itis inspired by previous methods, the SVD-transform and Eigen-transform, whichyield measures of image roughness by considering the singular values or eigenvaluesof matrices formed by copying greyvalues from a square patch arounda pixel directly into a matrix of the same size. The SVD and Eigen-transformstherefore capture the degree to which linear dependencies are present in the imagepatch. In this paper we demonstrate that similar information can be recovered byexamining the properties of the LU factorization of the matrix, and in particularthe diagonal part of the U matrix. While the LU-transform yields an output qualitativelysimilar to the those of the SVD and Eigen-transforms, it can be computedabout an order of magnitude faster. It is a much simpler algorithm and well-suitedto implementation on parallel architectures. We capitalise on these properties inan implementation of the algorithm on a Graphics Processor Unit (GPU) whichmakes it even faster than a CPU implementation, and frees the CPU for othercomputations.

  • 30.
    Uhlin, Tomas
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Nordlund, Peter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Maki, Atsuto
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Towards an Active Visual Observer1995In: Computer Vision, 1995. Proceedings., Fifth International Conference on, 1995, p. 679-686Conference paper (Refereed)
    Abstract [en]

    We present a binocular active vision system that can attend to and fixate a moving target. Our system has an open and expandable design and it forms the first steps of a long term effort towards developing an active observer using vision to interact with the environment, in particular capable of figure-ground segmentation. We also present partial real-time implementations of this system and show their performance in real-world situations together with motor control. In pursuit we particularly focus on occlusions of other targets, both stationary and moving, and integrate three cues, ego- motion, target motion and target disparity, to obtain an overall robust behavior. An active vision system must be open, expandable, and operate with whatever data are available momen- tarily. It must also be equipped with means and meth- ods to direct and change its attention. This system is therefore equipped with motion detection for changing attention and pursuit for maintaining attention, both of which run concurrently.

  • 31.
    Vicente, Isabel Serrano
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Learning and recognition of object manipulation actions using linear and nonlinear dimensionality reduction2007In: 2007 RO-MAN: 16TH IEEE  International Symposium On Robot And Human Interactive Communication, Vols 1-3, 2007, p. 1003-1008Conference paper (Refereed)
    Abstract [en]

    In this work, we perform an extensive statistical evaluation for learning and recognition of object manipulation actions. We concentrate on single arm/hand actions but study the problem of modeling and dimensionality reduction for cases where actions are very similar to each other in terms of arm motions. For this purpose, we evaluate a linear and a nonlinear dimensionality reduction techniques: Principal Component Analysis and Spatio-Temporal Isomap. Classification of query sequences is based on different variants of Nearest Neighbor classification. We thoroughly describe and evaluate different parameters that affect the modeling strategies and perform the evaluation with a training set of 20 people.

1 - 31 of 31
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