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检索条件"机构=Computer Vision and Active Perception Laboratory"

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Integrating representative and discriminant models for object category detection

Integrating representative and discriminant models for objec...

引用

International Conference on computer vision (ICCV)

作者： M. Fritz B. Leibe B. Caputo B. Schiele Multimodal Interactive Systems Technical University of Darmstadt Germany Computer Vision and Active Perception Laboratory KTH Stockholm Sweden

Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results

关键词： Object detection Kernel Image segmentation Shape Voting Support vector machines Support vector machine classification computer vision Object recognition Visual databases

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Integrating object and grasp recognition for dynamic scene interpretation

Integrating object and grasp recognition for dynamic scene i...

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International Conference on Advanced Robotics (ICAR)

作者： S. Ekvall D. Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Department of Numerical Analysis and Computer Science Royal Institute of Technology Stockholm Sweden

Understanding and interpreting dynamic scenes and activities is a very challenging problem. In this paper, we present a system capable of learning robot tasks from demonstration. Classical robot task programming requires an experienced programmer and a lot of tedious work. In contrast, programming by demonstration is a flexible framework that reduces the complexity of programming robot tasks, and allows end-users to demonstrate the tasks instead of writing code. We present our recent steps towards this goal. A system for learning pick-and-place tasks by manually demonstrating them is presented. Each demonstrated task is described by an abstract model involving a set of simple tasks such as what object is moved, where it is moved, and which grasp type was used to move it

关键词： Layout Robot programming Writing Humans Sensor phenomena and characterization Object recognition computer vision Laboratories Numerical analysis computer science

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Object recognition using composed receptive field histograms of higher dimensionality

Object recognition using composed receptive field histograms...

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International Conference on Pattern Recognition

作者： O. Linde T. Lindeberg Department of Numerical Analysis and Computer Science KTH Computational Vision and Active Perception Laboratory Stockholm Sweden

Effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. This work presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by Swain & Bollard (1991) or Schiele & Crowley (2000). The use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. Results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of Gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. It is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. Experiments are also reported of classifying unknown objects into visual categories.

关键词： Object recognition Histograms Image recognition Statistics Wavelet coefficients computer vision Laboratories Numerical analysis computer science Councils

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Combination of foveal and peripheral vision for object recognition and pose estimation

Combination of foveal and peripheral vision for object recog...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： M. Bjorkman D. Kragic Centre for Autonomous Systems & Computer Vision and Active Perception Laboratory Royal Institute of Technology Stockholm Sweden

In this paper, we present a real-time vision system that integrates a number of algorithms using monocular and binocular cues to achieve robustness in realistic settings, for tasks such as object recognition, tracking and pose estimation. The system consists of two sets of binocular cameras; a peripheral set for disparity based attention and a foveal one for higher level processes. Thus the conflicting requirements of a wide field of view and high resolution can be overcome. One important property of the system is that the step from task specification through 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.

关键词： Object recognition Cameras Machine vision Robustness Layout Image segmentation Image recognition computer vision Real time systems Solid modeling

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Cue integration through discriminative accumulation

Cue integration through discriminative accumulation

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M.E. Nilsback B. Caputo Department of Numerical Analysis and Computer Science Computational Vision and Active Perception Laboratory (CVAP) KTH Stockholm Sweden

Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for support vector machines (SVMs), but we also show that it is easily extendible to any large margin classifier. In the case of one-class SVMs the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.

关键词： Voting Object recognition Robustness computer vision Histograms Humans Laboratories Numerical analysis computer science Testing

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Recognizing human actions: a local SVM approach

Recognizing human actions: a local SVM approach

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International Conference on Pattern Recognition

作者： C. Schuldt I. Laptev B. Caputo Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.

关键词： Humans Support vector machines computer vision Pattern recognition Support vector machine classification Cameras Frequency Spatial databases Performance evaluation Image recognition

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Velocity adaptation of space-time interest points

Velocity adaptation of space-time interest points

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International Conference on Pattern Recognition

作者： I. Laptev T. Lindeberg Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

The notion of local features in space-time has recently been proposed to capture and describe local events in video. When computing space-time descriptors, however, the result may strongly depend on the relative motion between the object and the camera. To compensate for this variation, we present a method that automatically adapts the features to the local velocity of the image pattern and, hence, results in a video representation that is stable with respect to different amounts of camera motion. Experimentally we show that the use of velocity adaptation substantially increases the repeatability of interest points as well as the stability of their associated descriptors. Moreover, for an application to human action recognition we demonstrate how velocity-adapted features enable recognition of human actions in situations with unknown camera motion and complex, non-stationary backgrounds.

关键词： Cameras Humans Image motion analysis Pattern recognition Motion measurement Optical recording Covariance matrix computer vision Laboratories Numerical analysis

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Galilean-diagonalized spatio-temporal interest operators

Galilean-diagonalized spatio-temporal interest operators

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International Conference on Pattern Recognition

作者： T. Lindeberg A. Akbarzadeh I. Laptev Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents a set of image operators for detecting regions in space-time where interesting events occur. To define such regions of interest, we compute a spatio-temporal second-moment matrix from a spatio-temporal scale-space representation, and diagonalize this matrix locally, using a local Galilean transformation in space-time, optionally combined with a spatial rotation, so as to make the Galilean invariant degrees of freedom explicit. From the Galilean-diagonalized descriptor so obtained, we then formulate different types of space-time interest operators, and illustrate their properties on different types of image sequences.

关键词： Cameras Covariance matrix computer vision Laboratories Numerical analysis computer science Image sequences Layout Councils Filters

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Many-to-many feature matching using spherical coding of directed graphs 8

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8th European Conference on computer vision, ECCV 2004

作者： Fatili Demirci, M. Shokoufandeh, Ali Dickinson, Sven Keselman, Yakov Bretzner, Lars Department of Computer Science Drexel University PhiladelphiaPA19104 United States Department of Computer Science University of Toronto TorontoONM5S 3G4 Canada School of Computer Science Telecommunications and Information Systems DePaul University ChicagoIL60604 United States Computational Vision and Active Perception Laboratory Department Of Numerical Analysis and Computer Science KTH Stockholm Sweden

ISBN: (纸本)3540219846

Abstract. In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge-weighted directed graph. The algorithm was based on a metric-tree representation of labeled graphs and their metric embedding into normed vector spaces, using the embedding algorithm of Matousek [13]. However, the method was limited by the fact that two graphs to be matched were typically embedded into vector spaces with different dimensionality. Before the embeddings could be matched, a dimensionality reduction technique (PCA) was required, which was both costly and prone to error. In this paper, we introduce a more efficient embedding procedure based on a spherical coding of directed graphs. The advantage of this novel embedding technique is that it prescribes a single vector space into which both graphs are embedded. This reduces the problem of directed graph matching to the problem of geometric point matching, for which efficient many-to-many matching algorithms exist, such as the Earth Mover's Distance. We apply the approach to the problem of multi-scale, view-based object recognition, in which an image is decomposed into a set of blobs and ridges with automatic scale selection. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Directed graphs

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Fast shape-based road sign detection for a driver assistance system

Fast shape-based road sign detection for a driver assistance...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： G. Loy N. Barnes Computer Vision and Active Perception Laboratory Royal Institute of Technology (KTH) Stockholm Sweden Autonomous Systems and Sensor Technologies Program National ICT Australia Canberra Australia

A new method is presented for detecting triangular, square and octagonal road signs efficiently and robustly. The method uses the symmetric nature of these shapes, together with the pattern of edge orientations exhibited by equiangular polygons with a known number of sides, to establish possible shape centroid locations in the image. This approach is invariant to in-plane rotation and returns the location and size of the shape detected. Results on still images show a detection rate of over 95%. The method is efficient enough for real-time applications, such as on-board-vehicle sign detection.

关键词： Shape Road safety Detectors Image segmentation Robustness Image edge detection Australia Pixel Voting computer vision

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