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Visual attention using game theory 2

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2nd International Workshop on Biologically Motivated computer vision, BMCV 2002

作者： Ramström, Ola Christensen, Henrik I. Computational Vision and Active Perception Numerical Analysis and Computer Science Royal Institute of Technology StockholmSE-100 44 Sweden

ISBN: (纸本)9783540001744

A system using visual information to interact with its environment, e.g. a robot, needs to process an enormous amount of data. To ensure that the visual process has tractable complexity visual attention plays an important role. A visual process will always have a num.er of implicit and explicit tasks that defines its purpose. The present document discusses attention mechanisms for selection of visual input to respond to the current set of tasks. To provide a truly distributed approach to attention it is suggested to model the control using game theory, in particular coalition games. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Game theory

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A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration

A robust and convergent iterative approach for determining t...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： P. Fornland C. Schnorr Computational Vision and Active Perception Num. Analysis and Computer Science Royal Institute of Technology Stockholm Sweden AB Kogs FB Informatik University of Hamburg Hamburg Germany

A robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. Neither camera calibration nor stereo correspondence is required. Recently L. Cohen (1996) formalized a framework guaranteeing (local) convergence of iterative two-step methods. In this paper, the framework is adopted, with a global step using tentative matches to estimate the planar projectivity, and a local step attempting to solve the stereo correspondence. A detected point in the first image is matched to an auxiliary point in the second image, on the line joining the transformed first image point, and its closest detected second image point. Convergence is assured, while achieving robustness to both mismatching and non-coplanar points.

关键词： Robustness Iterative methods Convergence Calibration Cameras Robot kinematics computer vision Layout Robot vision systems Image reconstruction

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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 num.rical analysis computer science Councils

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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 num.er 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 num.rical analysis computer science Testing

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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 num.rical analysis

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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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Integrating task-directed plaiming with reactive object recognition 12

Integrating task-directed plaiming with reactive object reco...

引用

Intelligent Robots and computer vision XII: Algorithms and Techniques 1993

作者： Dickinson, Sven Stevenson, Suzanne Amdur, Eugene Tsotsos, John Olsson, Lars Department of Computer Science University of Toronto 6 King's College Rd. TorontoONM5S1A4 Canada Computational Vision and Active Perception Laboratory Dept. of Numerical Analysis and Computing Science Royal Institute of Tecimology StockholmS-10044 Sweden

We describe a robot vision system that achieves complex object recognition with two layers of behaviors, performing the tasks of planning and object recognition, respectively. The recognition layer is a pipeline in which successive stages take in images from a stereo head, recover relevant features, build intermediate representations, and deposit 3-D objects into a world model. Each stage is an independent process that reacts automatically to output from the previous stage. This reactive system operates continuously and autonomously to construct the robot's 3-D model of the environment. Sitting above the recognition pipeline is the planner which is responsible for populating the world model with objects that satisfy the high-level goals of the system. For example, upon examination of the world model, the planner can decide to direct the head to another location, gating new images into the recognition pipeline, causing new objects to be deposited into the world model. Alternatively, the planner can alter the recognition behavior of the pipeline so that objects of a certain type or at a certain location appear in the world model. © 1993 SPIE.

关键词： Pipelines

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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 num.rical analysis computer science Image sequences Layout Councils Filters

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

引用

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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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 num.rical analysis computer science

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