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Distributed segmentation and classification of human actions using a wearable motion sensor network

Distributed segmentation and classification of human actions...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： A.Y. Yang S. Iyengar S. Sastry R. Bajcsy P. Kuryloski R. Jafari Department of EECS University of California Berkeley USA Department of ECE Cornell University USA Department of EE University of Technology Dallas USA

We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. We show that the distribution of multiple action classes satisfies a mixture subspace model, one sub-space for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the representation. We further provide fast linear solvers to compute such representation via l 1 -minimization. Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework.

关键词： Humans Wearable sensors Sensor systems Base stations Accelerometers Wireless sensor networks computer networks Biomedical monitoring Distributed computing Testing

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A case for the average-half-face in 2D and 3D for face recognition

A case for the average-half-face in 2D and 3D for face recog...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Josh Harguess J. K. Aggarwal Computer & Vision Research Center/ Department of ECE University of Texas Austin USA

We observe that the human face is inherently symmetric and we would like to exploit this symmetry in face recognition. The average-half-face has been previously shown to do just that for a set of 3D faces when using eigenfaces for recognition. We build upon that work and present a comparison of the use of the average-half-face to the use of the original full face with 6 different algorithms applied to two- and three-dimensional (2D and 3D) databases. The average-half-face is constructed from the full frontal face image in two steps; first the face image is centered and divided in half and then the two halves are averaged together (reversing the columns of one of the halves). The resulting average-half-face is then used as the input for face recognition algorithms. Previous work has shown that the accuracy of 3D face recognition using eigenfaces with the average-half-face is significantly better than using the full face. We compare the results using the average-half-face and the full face using six face recognition methods; eigenfaces, multi-linear principal components analysis (MPCA), MPCA with linear discriminant analysis (MPCALDA), Fisherfaces (LDA), independent component analysis (ICA), and support vector machines (SVM). We utilize two well-known 2D face database as well as a 3D face database for the comparison. Our results show that in most cases it is superior to employ the average-half-face for frontal face recognition. The consequences of this discovery may result in substantial savings in storage and computation time.

关键词： Face recognition Principal component analysis Linear discriminant analysis Independent component analysis Support vector machines Image databases Biometrics Scattering computer vision Humans

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Removing pedestrians from Google street view images

Removing pedestrians from Google street view images

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Arturo Flores Serge Belongie Department of Computer Science and Engineering University of California San Diego USA

Since the introduction of Google Street View, a part of Google Maps, vehicles equipped with roof-mounted mobile cameras have methodically captured street-level images of entire cities. The worldwide Street View coverage spans over 10 countries in four different continents. This service is freely available to anyone with an internet connection. While this is seen as a valuable service, the images are taken in public spaces, so they also contain license plates, faces, and other information information deemed sensitive from a privacy standpoint. Privacy concerns have been expressed by many, in particular in European countries. As a result, Google has introduced a system that automatically blurs faces in Street View images. However, many identifiable features still remain on the un-blurred person. In this paper, we propose an automatic method to remove entire pedestrians from Street View images in urban scenes. The resulting holes are filled in with data from neighboring views. A compositing method for creating “ghost-free” mosaics is used to minimize the introduction of artifacts. This yields Street View images as if the pedestrians had never been there. We present promising results on a set of images from cities around the world.

关键词： Layout Privacy Cities and towns Licenses Cameras computer science Automotive engineering Vehicles Mobile computing Continents

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A study relating image sampling rate and image pattern recognition

A study relating image sampling rate and image pattern recog...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Srikantan Srihari CEDAR Amherst NY USA

The trade-off between the granularity of the data representation and the recognition accuracy is examined in this paper. We show that unless a particular criterion is satisfied, there is no guarantee of achieving a prescribed recognition accuracy. This criterion of interest, is the mutual information content between the measurements and the class identities. A novel method for the objective evaluation of intrinsic error in recognition as sampling rate varies, is described. This approach is general enough to permit the evaluation of error even when the parameter under study takes a different form. To demonstrate this we present results in feature subset selection and multiple classifier combination. In the case of feature selection, the measurements are the features. In the case of multiple classifier combination it is the "quality" of the individual classifiers, evalulated based on the mutual information between the classifier parameters and class identities.< >

关键词： Data structures pattern recognition Image sampling Feature extraction

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Revisiting overlap invariance in medical image alignment

Revisiting overlap invariance in medical image alignment

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nathan D. Cahill Julia A. Schnabel J. Alison Noble David J. Hawkes Wolfson Medical Vision Laboratory University of Oxford Oxford UK Research & Innovation Carestream Health Inc. Rochester NY USA Centre for Medical Image Computing University College London London UK

In Studholme et al. introduced normalized mutual information (NMI) as an overlap invariant generalization of mutual information (MI). Even though Studholme showed how NMI could be used effectively in multimodal medical image alignment, the overlap invariance was only established empirically on a few simple examples. In this paper, we illustrate a simple example in which NMI fails to be invariant to changes in overlap size, as do other standard similarity measures including MI, cross correlation (CCorr), correlation coefficient (CCoeff), correlation ratio (CR), and entropy correlation coefficient (ECC). We then derive modified forms of all of these similarity measures that are proven to be invariant to changes in overlap size. This is done by making certain assumptions about background statistics. Experiments on multimodal rigid registration of brain images show that 1) most of the modified similarity measures outperform their standard forms, and 2) the modified version of MI exhibits superior performance over any of the other similarity measures for both CT/MR and PET/MR registration.

关键词： Biomedical imaging Mutual information Measurement standards Size measurement Chromium Entropy Statistics Brain Computed tomography Positron emission tomography

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Protein Interaction Inference as a MAX-SAT Problem

Protein Interaction Inference as a MAX-SAT Problem

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ya Zhang Hongyuan Zha Chao-Hisen Chu Xiang Ji School of Information Sciences and Technology USA Department of Computer Science and Engineering Pennsylvania State University USA NEC Laboratories of America Inc. USA

Discovering interacting proteins is essential for understanding protein functions. However, high throughput interaction data are inherently noisy and only cover a small portion of the whole interactome. Domains, the building block of proteins, are believed to be responsible for the interactions among proteins. An abstract representation of interactome is achieved at domain level and this representation also facilitates the discovery of unobserved proteinprotein interactions. Many domain-based approaches have been proposed to predict protein-protein interactions and promising results have been obtained. Existing methods generally assume that domain interactions are independent of each other for the convenience of computational modeling. In this paper, a new framework of learning is proposed. The framework makes no assumption about domain interactions and consider protein interactions resulting from multiple domain interactions which may be dependent of each other. With a conjunctive normal form representation of the relationship between protein interactions and domain interactions, the problem of interaction inference is modeled as a constraint satis?ability problem and solved via linear programming. Experimental results on a combined yeast data set have demonstrated the robustness of and the accuracy of the proposed algorithm.

关键词： Protein engineering Throughput Computational modeling Fungi Proteomics Genomics Bioinformatics Chaos computer science National electric code

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vision SYSTEMS FOR PCB MANUFACTURING IN JAPAN

VISION SYSTEMS FOR PCB MANUFACTURING IN JAPAN

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16TH ANNUAL CONF OF THE ieee INDUSTRIAL ELECTRONICS SOC ( IECON 90 )

作者： HATA, S Hitachi Ltd Japan

ISBN: (纸本)0879426004

Current vision systems in Japanese printed circuit board (PCB) manufacturing lines are described. vision systems have been introduced into almost every level of PCB manufacturing in Japan. They include mask pattern inspection machines, PCB pattern inspection machines, surface-mount device (SMD) mounters with visual positioning, mounted SMD visual inspection machines, soldering inspection machines, and assembled PCB visual inspection machines. Most of these vision systems were integrated successfully and achieved significant benefits. Rapid development of SMDs necessitated the development of new types of mounters and visual inspection systems.

关键词： computer vision

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Recursive compositional models: Representation, learning, and inference

Recursive compositional models: Representation, learning, an...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Long Zhu Alan Yuille CSAIL-MIT USA Department Statistics Psychology and Computer Science UCLA USA

Recursive compositional models (RCMs) are hierarchical models which enable us to represent the shape/geometry and visual appearance of objects and images at different scales. The key design principle is recursive compositionality. Objects are represented by RCMs in a hierarchical form where complex structures are composed of more elementary structures. Formally, they are represented by probability distributions defined over graphs with variable topology. Learning techniques are used to learn these models from a limited number of examples of the object by exploiting the recursive structure (some of our papers use supervised learning while others are unsupervised and induce the object structure). In addition, we can exploit this structure to develop algorithms that can perform inference on these RCMs to rapidly detect and recognize objects. This differs from more standard “flat models” of objects which have much less representational power if they wish to maintain efficient learning and inference. The basic properties of an RCM are illustrated in figures (1, 2). Because RCMs give a rich hierarchical description of objects and images they can be applied to a range of tasks including object detection, segmentation, parsing and image parsing. In all cases, we achieved state of the art results when evaluated on datasets with groundtruth.

关键词： Topology Solid modeling Shape Object detection Statistics Psychology computer science Computational geometry Information geometry Probability distribution

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A novel segmentation method for MR brain images based on fuzzy connectedness and FCM

A novel segmentation method for MR brain images based on fuz...

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2nd International conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

作者： Fan, Xian Yang, Jie Cheng, Lishui Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 200030 Shanghai China

ISBN: (纸本)9783540283126

Image segmentation is an important research topic in image processing and computer vision community. In this paper, a new unsupervised method for MR brain image segmentation is proposed based on fuzzy c-means (FCM) and fuzzy connectedness. FCM is a widely used unsupervised clustering algorithm for pattern recognition and image processing problems. However, FCM does not consider the spatial coherence of images and is sensitive to noise. On the other hand, fuzzy connectedness method has achieved good performance for medical image segmentation. However, in the computation of fuzzy connectedness, one needs to select seeds manually which is elaborative and time-consuming. Our new method used FCM as the first step to select salient seeded points and then applied fuzzy connectedness algorithm based on those seeds. Thus our method achieved unsupervised automatic segmentation for brain MR images. Experiments on simulated and real data sets proved it is effective and robust to noise. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image segmentation

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X-Y separable pyramid steerable scalable kernels

X-Y separable pyramid steerable scalable kernels

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shy Perona California Institute of Technology Pasadena CA USA California Institute of Technology Universita di Padova Pasadena CA USA

A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This "pseudo-SVD" improves upon a previous scheme due to Perona (1992) in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kernels which are suitable for use with a Laplacian pyramid. The properties of this method are illustrated experimentally in generating steerable and scalable approximations to an early vision edge-detection kernel.< >

关键词： Filtering Machine vision Image edge analysis Image orientation analysis

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