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Expectation grammars: leveraging high-level expectations for activity recognition

Expectation grammars: leveraging high-level expectations for...

引用

conference on computer vision and pattern recognition (cvpr)

作者： D. Minnen I. Essa T. Starner College of Computing Georgia Institute of Technology Atlanta GA USA

Video-based recognition and prediction of a temporally extended activity can benefit from a detailed description of high-level expectations about the activity. Stochastic grammars allow for an efficient representation of such expectations and are well-suited for the specification of temporally well-ordered activities. In this paper, we extend stochastic grammars by adding event parameters, state checks, and sensitivity to an internal scene model. We present an implemented system that uses human-specified grammars to recognize a person performing the Towers of Hanoi task from a video sequence by analyzing object interaction events. Experimental results from several videos show robust recognition of the full task and its constituent sub-tasks even though no appearance models of the objects in the video are provided. These experiments include videos of the task performed with different shaped objects and with distracting and extraneous interactions.

关键词： Stochastic processes Humans Poles and towers Hidden Markov models Layout Performance analysis Robustness Surveillance Educational institutions Video sequences

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Establishment shot detection using qualitative motion

Establishment shot detection using qualitative motion

引用

conference on computer vision and pattern recognition (cvpr)

作者： Loong Fah Cheong Yong Wang Hee Lin Wang Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)0769519008

In this paper, we developed a technique to detect a special kind of shot, namely establishment shot, which is used to introduce a new scene to the audience or remind the audience of a known environment. The detection of establishment shot aids in various middle-level issues such as analyzing the story units of a movie. An establishment shot is usually realized by using camera motions such as panning, tilting or lateral translation. As it possesses similar motion features to those of another kind of shot called object-tracking shot, the two can be easily confused. An object-tracking shot is important for indexing on its own; it keeps the target of interest in the field of view. In this paper, we developed a technique to differentiate these two kinds of shots. The technique developed does not require accurate egomotion estimation, and can handle arbitrary camera movement including zoom as well as multiple independent motions.

关键词： Gunshot detection systems Motion detection Cameras Layout computer vision Motion pictures Indexing Motion estimation Tracking Concurrent computing

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A performance evaluation of local descriptors

A performance evaluation of local descriptors

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conference on computer vision and pattern recognition (cvpr)

作者： K. Mikolajczyk C. Schmid INRIA Rhône-Alpes GRAVIR-CNRS Montbonnot Saint Martin France

In this paper we compare the performance of interest point descriptors. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest point detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the point detector. Our evaluation uses as criterion detection rate with respect to false positive rate and is carried out for different image transformations. We compare SIFT descriptors (Lowe, 1999), steerable filters (Freeman and Adelson, 1991), differential invariants (Koenderink ad van Doorn, 1987), complex filters (Schaffalitzky and Zisserman, 2002), moment invariants (Van Gool et al., 1996) and cross-correlation for different types of interest points. In this evaluation, we observe that the ranking of the descriptors does not depend on the point detector and that SIFT descriptors perform best. Steerable filters come second ; they can be considered a good choice given the low dimensionality.

关键词： Detectors Filters Robustness computer vision Image recognition pattern recognition Performance evaluation Photometry Image segmentation Image databases

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Robust data clustering

Robust data clustering

引用

conference on computer vision and pattern recognition (cvpr)

作者： L.N.F. Ana A.K. Jain Dept of Computer Science and Engineering Michigan State University USA

We address the problem of robust clustering by combining data partitions (forming a clustering ensemble) produced by multiple clusterings. We formulate robust clustering under an information-theoretical framework; mutual information is the underlying concept used in the definition of quantitative measures of agreement or consistency between data partitions. Robustness is assessed by variance of the cluster membership, based on bootstrapping. We propose and analyze a voting mechanism on pairwise associations of patterns for combining data partitions. We show that the proposed technique attempts to optimize the mutual information based criteria, although the optimality is not ensured in all situations. This evidence accumulation method is demonstrated by combining the well-known K-means algorithm to produce clustering ensembles. Experimental results show the ability of the technique to identify clusters with arbitrary shapes and sizes.

关键词： Robustness Mutual information Clustering algorithms Partitioning algorithms Shape computer science Data engineering pattern analysis Voting Analysis of variance

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Qualitative image based localization in indoors environments

Qualitative image based localization in indoors environments

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conference on computer vision and pattern recognition (cvpr)

作者： J. Kosecka Liang Zhou P. Barber Z. Duric Department of Computer Science George Mason University Fairfax VA USA

Man made indoor environments possess regularities, which can be efficiently exploited in automated model acquisition by means of visual sensing. In this context we propose an approach for inferring a topological model of an environment from images or the video stream captured by a mobile robot during exploration. The proposed model consists of a set of locations and neighborhood relationships between them. Initially each location in the model is represented by a collection of similar, temporally adjacent views, with the similarity defined according to a simple appearance based distance measure. The sparser representation is obtained in a subsequent learning stage by means of learning vector quantization (LVQ). The quality of the model is tested in the context of qualitative localization scheme by means of location recognition: given a new view, the most likely location where that view came from is determined.

关键词： Indoor environments Mobile robots Context modeling Principal component analysis Robot sensing systems Topology Navigation computer science Drives Streaming media

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引用

conference on computer vision and pattern recognition (cvpr)

作者： Y. Keller A. Averbuch Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Computer Science School of Mathematical Sciences Tel-Aviv University Tel-Aviv Israel

This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors at different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem, which is solved iteratively by considering the intensities of a single image. The location of the maxima of the second image's gradient are used as initialization. We are able to robustly estimate affine and projective global motions using 'coarse to fine' processing, even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.

关键词： Image registration Image sensors Robustness Magnetic resonance imaging Gradient methods computer science Sensor phenomena and characterization Remote sensing Biomedical imaging Computed tomography

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Optimal linear representations of images for object recognition

Optimal linear representations of images for object recognit...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiuwen Liu A. Srivastava K. Gallivan Department of Computer Science Florida State University Tallahassee FL USA Department of Statistics Florida State University Tallahassee FL USA School of Computational Science and Information Technology Florida State University Tallahassee FL USA

Simplicity of linear representations (of images) makes them a popular tool in imaging analysis applications such as object recognition and image classification. Although several linear representations, namely PCA (principal component analysis), ICA, and FDA (Fisher discriminant analysis), have frequently been used, these representations are generally far from optimal in terms of actual application performance. We argue that representations should be chosen with respect to the application and the databases involved. Fixing an application, say object recognition, and assuming that recognition performance is computable for any linear basis (given a classifier and a database), we propose a Monte Carlo simulated annealing method that leads to optimal linear representations by maximizing the recognition performance over all fixed-rank subspaces. We illustrate this method on two popular databases.

关键词： Object recognition Principal component analysis Image analysis Image classification Independent component analysis Performance analysis Image databases Monte Carlo methods Computational modeling Simulated annealing

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Estimating the photorealism of images: distinguishing paintings from photographs

Estimating the photorealism of images: distinguishing painti...

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conference on computer vision and pattern recognition (cvpr)

作者： F. Cutzu R. Hammoud A. Leykin Department of Computer Science Indiana University Bloomington IN USA

Automatic classification of an image as a photograph of a real-scene or as a painting is potentially useful for image retrieval and Web site filtering applications. The main contribution of the paper is the proposition of several features derived from the color, edge, and gray-scale-texture information of the image that effectively discriminate paintings from photographs. For example, we found that paintings contain significantly more pure-color edges, and that certain gray-scale-texture measurements (mean and variance of Gabor filters) are larger for photographs. Using a large set of images (12000) collected from different Web sites, the proposed features exhibit very promising classification performance (over 90%). A comparative analysis of the automatic classification results and psychophysical data is reported, suggesting that the proposed automatic classifier estimates the perceptual photorealism of a given picture.

关键词： Painting computer graphics Application software Psychology Layout Humans Cities and towns computer science Image retrieval Information filtering

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Texture classification: are filter banks necessary?

Texture classification: are filter banks necessary?

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Varma A. Zisserman Robotics Research Group Department of Engineering Science University of Oxford Oxford UK

We question the role that large scale filter banks have traditionally played in texture classification. It is demonstrated that textures can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3 /spl times/ 3 pixels square), and that this outperforms classification using filter banks with large support. We develop a novel texton based representation, which is suited to modeling this joint neighborhood distribution for MRFs. The representation is learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. The power of the method is demonstrated by classifying over 2800 images of all 61 textures present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank based classifiers such as Leung & Malik, Cula & Dana, and Varma & Zisserman.

关键词： Channel bank filters Filter bank Large-scale systems Robots Image databases Shape computer vision Histograms Image segmentation Markov random fields

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View invariants for human action recognition

View invariants for human action recognition

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conference on computer vision and pattern recognition (cvpr)

作者： V. Parameswaran R. Chellappa Center for Automation Research University of Maryland College Park MD USA

This paper presents two approaches for the representation and recognition of human action in video, aiming for view-point invariance. The paper first presents new results using a 2D approach presented earlier. Inherent limitations of the 2D approach are discussed and a new 3D approach that builds on recent work on 3D model-based invariants, is presented. Each action is represented as a unique curve in a 3D invariance space, surrounded by an acceptance volume ('action volume'). Given a video sequence, 2D quantities from the image are calculated and matched against candidate action volumes in a probabilistic framework. The theory is presented followed by results on arbitrary projections of motion-capture data which demonstrate a high degree of tolerance to viewpoint change.

关键词： Humans Video sequences Gunshot detection systems Motion analysis Automation Educational institutions Motion detection Machine learning Cameras Image processing

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