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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Cloth representation by shape from shading with shading primitives

Cloth representation by shape from shading with shading prim...

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conference on computer vision and pattern recognition (cvpr)

作者： Feng Han Song-Chun Zhu Department of Computer Science and Statistics University of California Los Angeles Los Angeles CA USA

ISBN: (纸本)0769523722

Cloth is a complex visual pattern with flexible 3D shape and illumination variations. Computing the 3D shape of cloth from a single image is of great interest to both computer graphics and vision researches. However, the acquisition of 3D cloth shape by shape from shading (SFS) is still a challenge. In this paper, we present a two-layer generative model for representing both the 2D cloth image and the 3D cloth surface. The first layer represents all the folds on cloth, which are called "shading primitives" in (Haddon and Forsyth, 1998), and thus captures the overall "skeleton structures" of cloth. We learn a number of typical 3D fold primitives using some training images obtained through photometric stereo. The 3D fold primitives yield a dictionary of 2D shading primitives/or cloth images. The second layer represents non-fold parts with very smooth (often flat) surface or shading, which interpolates the primitives in the first layer with a smoothness prior like conventional SFS. Then we present an algorithm called "cloth sketching" to find all the shading primitives on cloth image and simultaneously recover their 3D shape by fitting to the 3D fold primitives. Our sketch representation can be viewed as a 2-layer Markov random field (MRF), and it introduces some prior knowledge on the folds and has lower dimension and is more robust than the traditional shape-fmm-shading representation which assumes a MRF model on pixels. We show a number of experiments with satisfactory results in comparison to previous work.

关键词： Shape Lighting computer vision computer graphics Skeleton Photometry Dictionaries Surface fitting Markov random fields Robustness

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Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling

Overtaking Vehicle Detection Using Dynamic and Quasi-Static ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Junxian Wang G. Bebis R. Miller Computer Vision Laboratory University of Nevada Reno NV USA Vehicle Design R&A Department Ford Motor Company Limited Dearborn MI USA

Robust and reliable detection of overtaking vehicles is an important component of any on-board driver assistance system. Optical flow, with the abundant motion information present in image sequences, has been studied extensively for vehicle detection. However, using dense optical ?ow for vehicle detection is sensitive to shocks and vibrations of the mobile camera; image outliers caused by illumination changes; and high computational complexity. To improve vehicle detection performance and reduce computational complexity, we propose an efficient and robust methodology for overtaking vehicle detection based on homogeneous sparse optical flow and eigenspace modeling. Specifically, our method models the background into dynamic and quasi-static regions. Instead of using dense optical flow to model the dynamic parts of the background, we employ homogeneous sparse optical flow, which makes detection more robust to camera shocks and vibrations. Moreover, to make detection robust to illumination changes, we employ a blockbased eigenspace approach to represent quasi-static regions in the background. A region-based hysteresis-thresholding approach, augmented by a localized spatial segmentation procedure, attains a good tradeoff between true detections and false positives. The proposed methodology has been evaluated using challenging traf?c scenes illustrating good performance.

关键词： Vehicle detection Vehicle dynamics Optical sensors Robustness Image motion analysis Electric shock Cameras Lighting Computational complexity Vehicle driving

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Error-bounded B-spline curve approximation based on dominant point selection

Error-bounded B-spline curve approximation based on dominant...

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2nd conference on computer Graphics, Imaging, and vision: New Trends 2005

作者： Park, Hyungjun Lee, Joo-Haeng Department of Industrial Engineering Chosun University Korea Republic of Electronics and Telecommunications Research Institute Korea Republic of

ISBN: (纸本)0769523927

We propose a new method for error-bounded B-spline curve approximation based on dominant point selection. The method first selects from the given points initial dominant points that govern the overall shape of the point set. FainIt then computes a knot vector using the dominant points and performs B-spline curve fitting to all the given points. If the fitted B-spline curve cannot approximate the points "within a specified tolerance, the method selects more points as dominant points and repeats the curve fitting process. The knots are determined in each iterative process by averaging the parameters of the dominant points. The shape index of a point set is introduced to facilitate the dominant point selection during the iterative curve fitting process. Compared with conventional methods presented for B-spline curve approximation, the proposed method requires much less control points to approximate the given point set with the desired shape fidelity. © 2005 ieee.

关键词： pattern recognition

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Human activity recognition

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1st International conference on pattern recognition and Machine Intelligence

作者： Aggarwal, JK Univ Texas Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)3540305068

Motion is an important cue for the human visual system Mobiles have fascinated children, Zeno (circa 500 B.C.) studied moving arrows to pose a paradox and Zeke is investigating the human brain devoted to the understanding of motion. In computer vision research, motion has played an important role for the past thirty years. An important application of motion research has emerged in the past decade. This area is devoted to the study of people - facial expression recognition, gesture recognition, whole-body tracking and human activity recognition. The broad area is at times called "looking at people." In addition, study of human motion is of interest to number of disciplines including psychology, kinesiology, choreography, computer graphics and human-computer interaction. Recent interest in certain applications like surveillance, sports-video analysis, monitoring a car driver has heightened our interest in human activity recognition. A major goal of current computer vision research is to recognize and understand human motion, activities and continuous activity. Initially, we focused on tracking a single person;today we focus on tracking, recognizing and understanding interactions among several people, for example at an airport or at a subway station. Interpreting such a scene is complex, because similar configurations may have different contexts and meanings. In addition, occlusion and correspondence of body parts in an interaction present serious difficulties to understanding the activity. Prof. Aggarwal's interest in motion started with the study of motion of rigid planar objects and it gradually progressed to the study of human motion. The current work includes the study of interactions at the gross (blob) level and at the detailed (head, torso, arms and legs) level. The two levels present different problems in terms of observation and analysis. For blob level analysis, we use a modified Hough transform called the Temporal Spatio-Velocity transform to isolate pixels

关键词： Motion estimation

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Unsupervised Modeling of Signs Embedded in Continuous Sentences

Unsupervised Modeling of Signs Embedded in Continuous Senten...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： S. Nayak S. Sarkar B. Loeding Department of Computer Science & Engineering University of South Florida Tampa FL USA Department of Special Education University of South Florida Lakeland FL USA

The common practice in sign language recognition is to ?rst construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use it to recognize signs in continuous sentences. In this paper we (i) propose a continuous state space model, where the states are based on purely image-based features, without the use of special gloves, and (ii) present an unsupervised approach to both extract and learn models for continuous basic units of signs, which we term as signemes, from continuous sentences. Given a set of sentences with a common sign, we can automatically learn the model for part of the sign, or signeme, that is least affected by coarticulation effects. While there are coarticulation effects in speech recognition, these effects are even stronger in sign language. The model itself is in term of traces in a space of Relational Distributions. Each point in this space represents a Relational Distribution, capturing the spatial relationships between low-level features, such as edge points. We perform speed normalization and then incrementally extract the common sign between sentences, or signemes, with a dynamic programming framework at the core to compute warped distance between two subsentences. We test our idea using the publicly available Boston SignStream Dataset by building signeme models of 18 signs. We test the quality of the models by considering how well we can localize the sign in a new sentence. We also present preliminary results for the ability to generalize across signers.

关键词： Handicapped aids Hidden Markov models Speech recognition Automatic speech recognition State-space methods Lakes Testing computer science computer science education Continuing education

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Object class recognition using multiple layer boosting with heterogeneous features

Object class recognition using multiple layer boosting with ...

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conference on computer vision and pattern recognition (cvpr)

作者： Wei Zhang Bing Yu G.J. Zelinsky D. Samaras Department of Computer Science SUNY at Stony Brook Stony Brook NY USA Department of Psychology SUNY at Stony Brook Stony Brook NY USA

We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The first layer selects PCA-SIFT and shape context features and combines the two feature types to form a strong classifier. Although previous approaches have used either feature type to train an AdaBoost model, our approach is the first to combine these complementary sources of information into a single feature pool and to use Adaboost to select those features most important for class recognition. The second layer adds to these local and global descriptions information about the spatial relationships between features. Through comparisons to the training sample, we first find the most prominent local features in Layer I, then capture the spatial relationships between these features in Layer 2. Rather than discarding this spatial information, we therefore use it to improve the strength of our classifier. We compared our method to (R. Fergus et al., 2003, A. Opelt et al., 2004, J. Thureson et al., 2004) and in all cases our approach outperformed these previous methods using a popular benchmark for object class recognition (R. Fergus et al., 2003). ROC equal error rates approached 99%. We also tested our method using a dataset of images that better equates the complexity between object and non-object images, and again found that our approach outperforms previous methods.

关键词： Boosting Object recognition Shape Benchmark testing Target recognition Object detection Face detection computer science Psychology Context modeling

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Fast and Robust Background Updating for Real-time Traffic Surveillance and Monitoring

Fast and Robust Background Updating for Real-time Traffic Su...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： S.M. Bhandarkar Xingzhi Luo Department of Computer Science University of Georgia Athens GA USA

Background updating is an important aspect of dynamic scene analysis. Three critical problems: sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem, which arise frequently in realworld surveillance and monitoring systems, are addressed in the proposed scheme. The paper presents a multi-color model where multiple color clusters are used to represent the background at each pixel location. In the proposed background updating scheme, the updates to the mean and variance of each color cluster at each pixel location incorporate the most recently observed color values. Each cluster is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. The sleeping person problem is tackled by virtue of the observation that at a given pixel location, the time durations and recurrence frequencies of the color clusters representing temporarily static objects are smaller compared to those of color clusters representing the true background colors when measured over a sufficiently long temporal history. The camera perturbation problem is solved using a fast camera motion detection algorithm that allows the current background image to be registered with the background model maintained in memory. Sudden illumination changes are handled by using an adaptive histogram template whereas gradual illumination changes are automatically resolved with the adaptive background model. The background updating scheme is shown to be robust even when the scene is very busy and also computationally efficient, making it suitable for realtime traffic surveillance and monitoring systems. Experimental results on real traffic monitoring and surveillance videos are presented.

关键词： Robustness Surveillance Monitoring Traffic control Cameras Lighting Frequency measurement Time measurement Image analysis Weight measurement

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Scrutinizing frequent pattern discovery performance

Scrutinizing frequent pattern discovery performance

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21st International conference on Data Engineering, ICDE 2005

作者： Zaïane, Osmar R. El-Hajj, Mohammad Li, Yi Luk, Stella Department of Computing Science University of Alberta Edmonton Alta. Canada

ISBN: (纸本)0769522858

Benchmarking technical solutions is as important as the solutions themselves. Yet many fields still lack any type of rigorous evaluation. Performance benchmarking has always been an important issue in databases and has played a significant role in the development, deployment and adoption of technologies. To help assessing the myriad algorithms for frequent itemset mining, we built an open framework and testbed to analytically study the performance of different algorithms and their implementations, and contrast their achievements given different data characteristics, different conditions, and different types of patterns to discover and their constraints. This facilitates reporting consistent and reproducible performance results using known conditions. © 2005 ieee.

关键词： pattern recognition

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Activity recognition and abnormality detection with the switching hidden semi-Markov model

Activity recognition and abnormality detection with the swit...

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conference on computer vision and pattern recognition (cvpr)

作者： T.V. Duong H.H. Bui D.Q. Phung S. Venkatesh Department of Computing Curtin University of Technology Perth WA Australia Artificial Intelligence Center SRI International Inc. Menlo Park CA USA

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

关键词： Hidden Markov models Pervasive computing Humans Atomic layer deposition Buildings Artificial intelligence Learning Training data Computational modeling Aging

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Synthetic Aperture Focusing using a Shear-Warp Factorization of the Viewing Transform

Synthetic Aperture Focusing using a Shear-Warp Factorization...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： V. Vaish G. Garg E. Talvala E. Antunez B. Wilburn M. Horowitz M. Levoy Department of Computer Science University of Stanford Stanford CA USA Department of Electrical Engineering University of Stanford Stanford CA USA

Synthetic aperture focusing consists of warping and adding together the images in a 4D light field so that objects lying on a specified surface are aligned and thus in focus, while objects lying of this surface are misaligned and hence blurred. This provides the ability to see through partial occluders such as foliage and crowds, making it a potentially powerful tool for surveillance. If the cameras lie on a plane, it has been previously shown that after an initial homography, one can move the focus through a family of planes that are parallel to the camera plane by merely shifting and adding the images. In this paper, we analyze the warps required for tilted focal planes and arbitrary camera configurations. We characterize the warps using a new rank- 1 constraint that lets us focus on any plane, without having to perform a metric calibration of the cameras. We also show that there are camera configurations and families of tilted focal planes for which the warps can be factorized into an initial homography followed by shifts. This shear-warp factorization permits these tilted focal planes to be synthesized as efficiently as frontoparallel planes. Being able to vary the focus by simply shifting and adding images is relatively simple to implement in hardware and facilitates a real-time implementation. We demonstrate this using an array of 30 videoresolution cameras; initial homographies and shifts are performed on per-camera FPGAs, and additions and a final warp are performed on 3 PCs.

关键词： Focusing Cameras Surveillance Layout computer vision computer science Calibration Hardware Field programmable gate arrays Personal communication networks

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