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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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A Multi-Camera Pose Tracker for Assisting the Visually Impaired

A Multi-Camera Pose Tracker for Assisting the Visually Impai...

引用

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

作者： F. Dellaert S. Tariq Georgia Institute of Technology College of Computing Georgia Institute of Technology USA

6DOF Pose tracking is useful in many contexts, e.g., in augmented reality (AR) applications. In particular, we seek to assist visually impaired persons by providing them with an auditory interface to their environment through ***. For this purpose, accurate head tracking in mixed indoor/outdoor settings is the key enabling technology. Most of the work to date has concentrated on single-camera systems with a relatively small .eld of view, but this presents a fundamental limit on the accuracy of such systems. We present a multi-camera pose tracker that handles an arbitrary *** of cameras rigidly .xed to the object of interest. By using multiple cameras, we increase both the robustness and the accuracy by which a 6-DOF pose is tracked. However, in a multi-camera rig setting, earlier methods for determining the unknown pose from three world-tocamera correspondences are no longer applicable, as they all assume a common center of projection. In this paper, we present a RANSAC-based method that copes with this limitation and handles multi-camera rigs. In addition, we present quantitative results to serve as a design guide for full system deployments based on multicamera rigs. Our formulation is completely general, in that it handles an arbitrary, heterogeneous collection of cameras in any arbitrary ***.

关键词： Cameras computer vision Augmented reality Robustness CMOS image sensors Field programmable gate arrays Educational institutions Application software System testing CMOS process

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Human recognition Based on Face Profiles in Video

Human Recognition Based on Face Profiles in Video

引用

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

作者： Xiaoli Zhou B. Bhanu Center for Research in Intelligent Systems University of California Riverside Riverside CA USA

Face profile is an important aspect of face recognition and it provides a complementary structure of the face that is seen in the non-frontal view. In the past, several methods have been proposed to recognize face profiles in still images. However, face profile images that are captured at a distance by surveillance cameras usually are video sequences that have a low resolution. It is difficult to extract accurate face profile directly from a low-resolution video frame, which does not have many pixels on the face profile. The emphasis of this paper is to introduce a practical approach for human recognition by using high-resolution face profile images constructed from the low-resolution videos. We use both the spatial and temporal information present in a number of adjacent low-resolution frames of a video sequence to construct high-resolution face profile images. As the quality of high-resolution images relies on the correctness of image alignment between consecutive frames, an elastic registration algorithm is used for face profile image alignment. A match statistic is designed to detect and discard poorly aligned images which may degrade the quality of the high-resolution face profile image. After obtaining high-resolution face profile images, we use a dynamic time warping method for face profile recognition. A number of dynamic video sequences are tested to demonstrate the applicability and reliability of our method.

关键词： Humans Face recognition Face detection Image recognition Video sequences Surveillance Cameras Image resolution Statistics Degradation

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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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A high resolution grammatical model for face representation and sketching

A high resolution grammatical model for face representation ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zijian Xu Hong Chen Song-Chun Zhu Department of Statistics and Computer Science University of California Los Angeles USA

ISBN: (纸本)0769523722

In this paper we present a generative, high resolution face representation which extends the well-known active appearance model (AAM) with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth, (ii) the other layer divides the face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse representations in the dictionaries of face components and skin features depending on the complexity of the face. The selection is modulated by the grammatical rules through hidden "switch " variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of face at high resolution (256 /spl times/ 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit face communication in wireless platforms.

关键词： Dictionaries Skin Active appearance model Switches Rendering (computer graphics) Face Principal component analysis Active shape model Eyes Eyebrows

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A Prescreener for 3D Face recognition Using Radial Symmetry and the Hausdorff Fraction

A Prescreener for 3D Face Recognition Using Radial Symmetry ...

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

作者： M.L. Koudelka M.W. Koch T.D. Russ Sensor Exploitation Apps Sandia National Laboratories Albuquerque NM USA Security Technology Sandia National Laboratories Albuquerque NM USA

Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to "prescreen" face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.

关键词： Face recognition Image databases Facial features Probes Spatial databases Sensor systems Laboratories Shape Feature extraction Data mining

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition

Proceedings of the 2004 IEEE Computer Society Conference on ...

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conference on computer vision and pattern recognition (cvpr)

Presents the table of contents of the proceedings.

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Shape from shading: a well-posed problem?

Shape from shading: a well-posed problem?

引用

conference on computer vision and pattern recognition (cvpr)

作者： E. Prados O. Faugeras Vision Laboratory University of California Los Angeles Los Angeles USA INRIA Odyssée Laboratory Sophia-Antipolis France

Shape from shading is known to be an ill-posed problem. We show in this paper that if we model the problem in a different way than it is usually done, more precisely by taking into account the 1/r/sup 2/ attenuation term of the illumination, shape from shading becomes completely well-posed. Thus the shading allows to recover (almost) any surface from only one image (of this surface) without any additional data (in particular, without the knowledge of the heights of the solution at the local intensity "minima", contrary to [P. Dupuis et al. (1994), E. Prados et al. (2004), B. Horn (1986), E. Rouy et al. (1992), R. Kimmel et al. (2001)]) and without regularity assumptions (contrary to [J. Oliensis et al. (1993), R. Kimmel et al. (1995)], for example). More precisely, we formulate the problem as that of solving a new partial differential equation (PDE), we develop a complete mathematical study of this equation and we design a new provably convergent numerical method. Finally, we present results of our new shape from shading method on various synthetic and real images.

关键词： Shape Light sources Cameras Lighting Partial differential equations Reflectivity Layout H infinity control Optical attenuators Attenuation

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Rank-R approximation of tensors using image-as-matrix representation

Rank-R approximation of tensors using image-as-matrix repres...

引用

conference on computer vision and pattern recognition (cvpr)

作者： H. Wang Narendra Ahuja Beckman Institute University of Illinois Urbana-Champaign USA

We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in traditional dimensionality reduction techniques like PCA, and higher-dimensional data as a tensor. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. The challenges lie in the computational and memory requirements for large ensembles. Currently, there exists a rank-R approximation algorithm which, although applicable to any number of dimensions, is efficient for only low-rank approximations. For larger dimensionality reductions, the memory and time costs of this algorithm become prohibitive. We propose a novel algorithm, for rank-R approximations of third-order tensors, which is efficient for arbitrary R but for the important special case of 2D image ensembles, e.g. video. Both of these algorithms reduce redundancies present in all dimensions. Rank-R tensor approximation yields the most compact data representation among all known image-as-matrix methods. We evaluated the performance of our algorithm vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification.

关键词： Tensile stress Principal component analysis Covariance matrix Least squares approximation Redundancy Algebra Image coding computer vision Facial animation Face recognition

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Semi-supervised cross feature learning for semantic concept detection in videos

Semi-supervised cross feature learning for semantic concept ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Rong Yan M. Naphade School of Computer Science Carnegie Mellon University Pittsburgh PA USA IBM Thomas J. Watson Research Center Hawthorne NY USA

For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose a novel multi-view semi-supervised learning algorithm called semi-supervised cross feature learning (SCFL). The proposed algorithm has two advantages over co-training. First, SCFL can theoretically guarantee its performance not being significantly degraded even when the assumption of view sufficiency fails. Also, SCFL can also handle additional views of unlabeled data even when these views are absent from the training data. As demonstrated in the TRECVID '03 semantic concept extraction task, the proposed SCFL algorithm not only significantly outperforms the conventional co-training algorithms, but also comes close to achieving the performance when the unlabeled set were to be manually annotated and used for training along with the labeled data set.

关键词： Videos Semisupervised learning Data mining Speech Gunshot detection systems computer science Drives Large-scale systems Degradation Training data

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OBJ CUT

OBJ CUT

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.P. Kumar P.H.S. Ton A. Zisserman Dept. of Computing Oxford Brookes University Oxford Brookes University Oxford Oxfordshire GB Dept. of Comput. Oxford Brookes Univ. UK Dept. of Engineering Science University of Oxford UK

In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.

关键词： Image segmentation Shape Object recognition Optimization methods Bayesian methods Markov random fields Horses Object detection Image sampling Cows

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