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  • 1. 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.

  • 2. Ekvall, S.
    et al.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hoffmann, F.
    Object recognition and pose estimation using color cooccurrence histograms and geometric modeling2005In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 23, no 11, p. 943-955Article in journal (Refereed)
    Abstract [en]

    Robust techniques for object recognition and pose estimation are essential for robotic manipulation and object grasping. In this paper, a novel approach for object recognition and pose estimation based on color cooccurrence histograms and geometric modelling is presented. The particular problems addressed are: (i) robust recognition of objects in natural scenes, (ii) estimation of partial pose using an appearance based approach, and (iii) complete 6DOF model based pose estimation and tracking using geometric models. Our recognition scheme is based on the color cooccurrence histograms embedded in a classical learning framework that facilitates a 'winner-takes-all' strategy across different views and scales. The hypotheses generated in the recognition stage provide the basis for estimating the orientation of the object around the vertical axis. This prior, incomplete pose information is subsequently made precise by a technique that facilitates a geometric model of the object to estimate and continuously track the complete 6DOF pose of the object. Major contributions of the proposed system are the ability to automatically initiate an object tracking process, its robustness and invariance towards scaling and translations as well as the computational efficiency since both recognition and pose estimation rely on the same representation of the object. The performance of the system is evaluated in a domestic environment with changing lighting and background conditions on a set of everyday objects.

  • 3. Garcia, Frederic
    et al.
    Aouada, Djamila
    Mirbach, Bruno
    Solignac, Thomas
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg.
    Unified multi-lateral filter for real-time depth map enhancement2015In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 41, p. 26-41Article in journal (Refereed)
    Abstract [en]

    This paper proposes a unified multi-lateral filter to efficiently increase the spatial resolution of low-resolution and noisy depth maps in real-time. Time-of-Flight (ToF) cameras have become a very promising alternative to stereo-based range sensing systems as they provide depth measurements at a high frame rate. However, there are actually two main drawbacks that restrict their use in a wide range of applications; namely, their fairly low spatial resolution as well as the amount of noise within the depth estimation. In order to address these drawbacks, we propose a new approach based on sensor fusion. That is, we couple a ToF camera of low-resolution with a 2-D camera of higher resolution to which the low-resolution depth map will be efficiently upsampled. In this paper, we first review the existing depth map enhancement approaches based on sensor fusion and discuss their limitations. We then propose a unified multi-lateral filter that accounts for the inaccuracy of depth edges position due to the low-resolution ToF depth maps. By doing so, unwanted artefacts such as texture copying and edge blurring are almost entirely eliminated. Moreover, the proposed filter is configurable to behave as most of the alternative depth enhancement approaches. Using a convolution-based formulation and data quantization and downsampling, the described filter has been effectively and efficiently implemented for dynamic scenes in real-time applications. The experimental results show a sensitive qualitative as well as quantitative improvement on raw depth maps, outperforming state-of-the-art multi-lateral filters. © 2015 Elsevier B.V. All rights reserved.

  • 4.
    Laptev, Ivan
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Lindeberg, Tony
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Velocity adaptation of spatio-temporal receptive fields for direct recognition of activities: an experimental study2004In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 22, no 2, p. 105-116Article in journal (Refereed)
    Abstract [en]

    This article presents an experimental study of the influence of velocity adaptation when recognizing spatio-temporal patterns using a histogram-based statistical framework. The basic idea consists of adapting the shapes of the filter kernels to the local direction of motion, so as to allow the computation of image descriptors that are invariant to the relative motion in the image plane between the camera and the objects or events that are studied. Based on a framework of recursive spatio-temporal scale-space, we first outline how a straightforward mechanism for local velocity adaptation can be expressed. Then, for a test problem of recognizing activities, we present an experimental evaluation, which shows the advantages of using velocity-adapted spatio-temporal receptive fields, compared to directional derivatives or regular partial derivatives for which the filter kernels have not been adapted to the local image motion.

  • 5.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A scale selection principle for estimating image deformations1998In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 16, p. 961-977Article in journal (Refereed)
    Abstract [en]

    A basic functionality of a vision system concerns the ability to compute deformation fields between different images of the same physical structure. This article advocates the need for incorporating explicit mechanisms for scale selection in this context, in algorithms for computing descriptors such as optic flow and for performing stereo matching. A basic reason why such a mechanism is essential is the fact that in a coarse-to-fine propagation of disparity or flow information, it is not necessarily the case that the most accurate estimates are obtained at the finest scales. The existence of interfering structures at fine scales may make it impossible to accurately match the image data at fine scales. selecting deformation estimates from the scales that minimize the (suitably normalized) uncertainty over scales. A specific implementation of this idea is presented for a region based differential flow estimation scheme. It is shown that the integrated scale selection and flow estimation algorithm has the qualitative properties of leading to the selection of coarser scales for larger size image structures and increasing noise level, whereas it leads to the selection of finer scales in the neighbourhood of flow field discontinuities

  • 6.
    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.
    The Scale-Space Primal Sketch: Construction and Experiments1992In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 10, no 1, p. 3-18Article in journal (Refereed)
    Abstract [en]

    We present a multi-scale representation of grey-level shape, called the 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 intuitively reasonable results.

  • 7.
    Lindeberg, Tony
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Gårding, Jonas
    Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure1997In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 15, no 6, p. 415-434Article in journal (Refereed)
    Abstract [en]

    This article describes a method for reducing the shape distortions due to scale-space smoothing that arise in the computation of 3-D shape cues using operators (derivatives) defined from scale-space representation. More precisely, we are concerned with a general class of methods for deriving 3-D shape cues from a 2-D image data based on the estimation of locally linearized deformations of brightness patterns. This class constitutes a common framework for describing several problems in computer vision (such as shape-from-texture, shape-from disparity-gradients, and motion estimation) and for expressing different algorithms in terms of similar types of visual front-end-operations. It is explained how surface orientation estimates will be biased due to the use of rotationally symmetric smoothing in the image domain. These effects can be reduced by extending the linear scale-space concept into an affine Gaussian scalespace representation and by performing affine shape adaptation of the smoothing kernels. This improves the accuracy of the surface orientation estimates, since the image descriptors, on which the methods are based, will be relative invariant under affine transformations, and the error thus confined to the higher-order terms in the locally linearized perspective transformation. A straightforward algorithm is presented for performing shape adaptation in practice. Experiments on real and synthetic images with known orientation demonstrate that in the presence of moderately high noise levels the accuracy is improved by typically one order of magnitude.

  • 8.
    Nalpantidis, Lazaros
    et al.
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Stereo vision for robotic applications in the presence of non-ideal lighting conditions2010In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 28, no 6, p. 940-951Article in journal (Refereed)
    Abstract [en]

    Many robotic and machine-vision applications rely on the accurate results of stereo correspondence algorithms. However, difficult environmental conditions, such as differentiations in illumination depending on the viewpoint, heavily affect the stereo algorithms' performance. This work proposes a new illumination-invariant dissimilarity measure in order to substitute the established intensity-based ones. The proposed measure can be adopted by almost any of the existing stereo algorithms, enhancing it with its robust features. The performance of the dissimilarity measure is validated through experimentation with a new adaptive support weight (ASW) stereo correspondence algorithm. Experimental results for a variety of lighting conditions are gathered and compared to those of intensity-based algorithms. The algorithm using the proposed dissimilarity measure outperforms all the other examined algorithms, exhibiting tolerance to illumination differentiations and robust behavior.

  • 9. Ormoneit, D.
    et al.
    Black, M. J.
    Hastie, T.
    Kjellström, Hedvig
    FOI, Stockholm.
    Representing cyclic human motion using functional analysis2005In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 23, no 14, p. 1264-1276Article in journal (Refereed)
    Abstract [en]

    We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

  • 10.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jie, Luo
    Caputo, Barbara
    The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition2010In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 28, no 7, p. 1080-1097Article in journal (Refereed)
    Abstract [en]

    The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [43] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.

  • 11. Romero, Javier
    et al.
    Kjellström, Hedvig
    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.
    Ek, Carl Henrik
    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.
    Non-parametric hand pose estimation with object context2013In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 31, no 8, p. 555-564Article in journal (Refereed)
    Abstract [en]

    In the spirit of recent work on contextual recognition and estimation, we present a method for estimating the pose of human hands, employing information about the shape of the object in the hand. Despite the fact that most applications of human hand tracking involve grasping and manipulation of objects, the majority of methods in the literature assume a free hand, isolated from the surrounding environment. Occlusion of the hand from grasped objects does in fact often pose a severe challenge to the estimation of hand pose. In the presented method, object occlusion is not only compensated for, it contributes to the pose estimation in a contextual fashion; this without an explicit model of object shape. Our hand tracking method is non-parametric, performing a nearest neighbor search in a large database (.. entries) of hand poses with and without grasped objects. The system that operates in real time, is robust to self occlusions, object occlusions and segmentation errors, and provides full hand pose reconstruction from monocular video. Temporal consistency in hand pose is taken into account, without explicitly tracking the hand in the high-dim pose space. Experiments show the non-parametric method to outperform other state of the art regression methods, while operating at a significantly lower computational cost than comparable model-based hand tracking methods.

  • 12.
    Zucchelli, Marco
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kosecka, Jana
    Motion bias and structure distortion induced by intrinsic calibration errors2008In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 26, no 5, p. 639-646Article in journal (Refereed)
    Abstract [en]

    This article provides an account of sensitivity and robustness of structure and motion recovery with respect to the errors in intrinsic parameters of the camera. We demonstrate both analytically and in simulation, the interplay between measurement and calibration errors and their effect on motion and structure estimates. In particular we show that the calibration errors introduce an additional bias towards the optical axis, which has opposite sign to the bias typically observed by egomotion algorithms. The overall bias causes a distortion of the resulting 3D structure, which we express in a parametric form. The analysis and experiments are carried out in the differential setting for motion and structure estimation from image velocities. While the analytical explanations are derived in the context of linear techniques for motion estimation, we verify our observations experimentally on a variety of optimal and suboptimal motion and structure estimation algorithms. The obtained results illuminate and explain the performance and sensitivity of the differential structure and motion recovery techniques in the presence of calibration errors.

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