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3D Facial Normalization with Spin Images and Influence of Range Data Calculation over Face Verification

3D Facial Normalization with Spin Images and Influence of Ra...

引用

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

作者： C. Conde A. Serrano Universidad Rey Juan Carlos (URJC) Face Recognition & Artificial Vision Group (FRAV) Madrid Spain

In this paper face verification techniques have been performed over 3D data acquired by a Laser Scanner. Advantages of 3D face models have been used to perform a normalization task over each face. First, an original method to detect local features points, based on Spin Images, has been developed. Once local features, as the nose tip or eyes corners, have been detected, a normalization process is carried out. After face normalization, different depth maps are calculated using several transform functions to equalize the images. The adequacy of each equalization to face verification has been measured to determine which one emphasizes most the feature discrimination. Face verification has been performed through a Principal Component Analysis and a Support Vector Machine. Final results show the importance of a careful 3D normalization and an optimal election of the depth map towards a improvement in the verification method.

关键词： Face detection Face recognition Laser modes computer vision Humans Data acquisition Databases Glass Nose Eyes

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computer vision for music identification

Computer vision for music identification

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yan Ke D. Hoiem R. Sukthankar School of Computer Science Carnegie Mellon University USA Intel Research Pittsburgh USA

We describe how certain tasks in the audio domain can be effectively addressed using computer vision approaches. This paper focuses on the problem of music identification, where the goal is to reliably identify a song given a few seconds of noisy audio. Our approach treats the spectrogram of each music clip as a 2D image and transforms music identification into a corrupted sub-image retrieval problem. By employing pairwise boosting on a large set of Viola-Jones features, our system learns compact, discriminative, local descriptors that are amenable to efficient indexing. During the query phase, we retrieve the set of song snippets that locally match the noisy sample and employ geometric verification in conjunction with an EM-based "occlusion" model to identify the song that is most consistent with the observed signal. We have implemented our algorithm in a practical system that can quickly and accurately recognize music from short audio samples in the presence of distortions such as poor recording quality and significant ambient noise. Our experiments demonstrate that this approach significantly outperforms the current state-of-the-art in content-based music identification.

关键词： computer vision Music information retrieval Spectrogram Image retrieval Boosting Indexing Phase noise Solid modeling Signal processing Audio recording

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Multilinear independent components analysis

Multilinear independent components analysis

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.A.O. Vasilescu D. Terzopoulos Department of Computer Science University of Toronto Toronto ONT Canada Courant Institute of Mathematical Sciences New York University New York NY USA

Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging. We introduce a nonlinear, multifactor model that generalizes ICA. Our multilinear ICA (MICA) model of image ensembles learns the statistically independent components of multiple factors. Whereas ICA employs linear (matrix) algebra, MICA exploits multilinear (tensor) algebra. We furthermore introduce a multilinear projection algorithm which projects an unlabeled test image into the N constituent mode spaces to simultaneously infer its mode labels. In the context of facial image ensembles, where the mode labels are person, viewpoint, illumination, expression, etc., we demonstrate that the statistical regularities learned by MICA capture information that, in conjunction with our multilinear projection algorithm, improves automatic face recognition.

关键词： Independent component analysis Lighting Principal component analysis Face recognition Algebra Projection algorithms Testing Higher order statistics Layout Matrices

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Learning feature distance measures for image correspondences

Learning feature distance measures for image correspondences

引用

conference on computer vision and pattern recognition (cvpr)

作者： X. Chen T.-J. Cham School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)0769523722

Standard but ad hoc measures such as sum-of-squared pixel differences (SSD) are often used when comparing and registering two images that have not been previously observed before. In this paper, we propose a framework to address the problem of learning a parametric feature distance measure to measure the dissimilarity between pairs of images. The method is based on optimizing the parameters of the distance measure in order to minimize correspondence classification errors on training data. Because the learning process involves relative (rather than absolute) visual content between image pairs, the learned distance measure may also be applied to other images with very different visual content. Results on matching classification with a wide variety of image content show that the learned feature distance measure clearly outperforms the standard measures of SSD, chamfer and Bhattacharyya histogram distances.

关键词： Training data Measurement standards Pixel Optimization methods Histograms Image matching Object detection Image retrieval Content based retrieval Testing

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Locally adaptive support-weight approach for visual correspondence search

Locally adaptive support-weight approach for visual correspo...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kuk-Jin Yoon In-So Kweon Robotics and Computer Vision Laboratory Department of Electrical Engineering and Computer Science KAIST Daejeon South Korea

ISBN: (纸本)0769523722

In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difficult and generally known as an NP-hard problem. For this reason, unlike the existing methods that try to find an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and support-weights of both support windows, and the correspondence is finally selected by the WTA (winner-takes-all) method. The experimental results for the rectified real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.

关键词： Shape Pixel Costs computer vision NP-hard problem Robot vision systems Laboratories computer science Area measurement Photometry

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Vehicle fingerprinting for reacquisition & tracking in videos

Vehicle fingerprinting for reacquisition & tracking in video...

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Guo S. Hsu Y. Shan H. Sawhney Rakesh Kumar Sarnoff Corporation Princeton NJ USA

Visual recognition of objects through multiple observations is an important component of object tracking. We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association. We employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. The challenges of change in pose, aspect and appearances across two disparate observations are handled by combining feature-based quasi-rigid alignment with flexible matching between two or more sequences. The current work uses the domain of vehicle tracking from aerial platforms where typically both the imaging platform and the vehicles are moving and the number of pixels on the object are limited to fairly low resolutions. Extensive evaluation with respect to ground truth is reported in the paper.

关键词： Vehicles Fingerprint recognition Videos Object recognition Feature extraction Data mining Pixel Image resolution Tracking Training data

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MosaicShape: stochastic region grouping with shape prior

MosaicShape: stochastic region grouping with shape prior

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jingbin Wang M. Betke Erdan Gu Computer Science Department Boston University MA USA Computer and Information Science Department University of Pennsylvania PA USA

A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework, and object segmentation and recognition are accomplished simultaneously by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized cluster sampling algorithm is presented in A. Barbu and S. Zhu (2003), combined with a large sampling jump and other implementation improvements, and greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

关键词： Stochastic processes Image sampling Image segmentation Shape measurement Object recognition Image retrieval Object segmentation Monte Carlo methods Clustering algorithms Image recognition

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Pose-robust face recognition using geometry assisted probabilistic modeling

Pose-robust face recognition using geometry assisted probabi...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiaoming Liu Tsuhan Chen General Electric Global Research Center Visualization and Computer Vision Laboratory Schenectady NY USA Advanced Multimedia Processing Lab Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)0769523722

Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition under pose variation, this paper presents a geometry assisted probabilistic approach. We approximate a human head with a 3D ellipsoid model, so that any face image is a 2D projection of such a 3D ellipsoid at a certain pose. In this approach, both training and test images are back projected to the surface of the 3D ellipsoid, according to their estimated poses, to form the texture maps. Thus the recognition can be conducted by comparing the texture maps instead of the original images, as done in traditional face recognition. In addition, we represent the texture map as an array of local patches, which enables us to train a probabilistic model for comparing corresponding patches. By conducting experiments on the CMU PIE database, we show that the proposed algorithm provides better performance than the existing algorithms.

关键词： Face recognition Geometry Solid modeling Ellipsoids Humans Lighting Head Testing Surface texture Image recognition

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Linear combination representation for outlier detection in motion tracking

Linear combination representation for outlier detection in m...

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conference on computer vision and pattern recognition (cvpr)

作者： G. Guo C.R. Dyer Z. Zhang Computer Sciences Department University of Wisconsin Madison Madison WI USA Microsoft Research Limited Redmond WA USA

In this paper we show that Ullman and Basri's linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outlier detection in motion tracking with an affine camera. For this task LC can be realized either on image frames or feature trajectories, and therefore two methods are developed which we call linear combination of frames and linear combination of trajectories. For robust estimation of the linear combination coefficients, the support vector regression (SVR) algorithm is used and compared with the RANSAC method. SVR based on quadratic programming optimization can efficiently deal with more than 50 percent outliers and delivers more consistent results than RANSAC in our experiments. The linear combination representation can use SVR in a straightforward manner while previous factorization-based or subspace separation methods cannot. Experimental results are presented using real video sequences to demonstrate the effectiveness of our LC+SVR approaches, including a quantitative comparison of SVR and RANSAC.

关键词： Motion detection Tracking Robustness Vectors Video sequences Object recognition Object detection Image sequences computer vision Cameras

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Multi-view geometry for general camera models

Multi-view geometry for general camera models

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conference on computer vision and pattern recognition (cvpr)

作者： P. Sturm GRAVIR-CNRS INRIA Rhône-Alpes Montbonnot France

We consider the structure-from-motion problem for a highly general imaging model where cameras are modeled as possibly unconstrained sets of projection rays. This allows to describe most existing camera types, including pinhole cameras, sensors with radial or more general distortions, catadioptric cameras (central or non-central), etc. We introduce a hierarchy of general camera models: the most general model has unconstrained projection rays whereas the most constrained model dealt with here is the central model, where all rays pass through a single point. Intermediate models are what we call axial cameras (all rays touch a single line), and x-slit cameras (rays touch two lines). The foundations for a multi-view geometry of completely non-central cameras are given, leading to the formulation of multi-view matching tensors, analogous to the fundamental/essential matrices, trifocal and quadrifocal tensors of perspective cameras. This framework is then specialized explicitly for the two-view case, for the intermediate camera types mentioned above.

关键词： Geometry Cameras Solid modeling Tensile stress Stereo vision Optical imaging Pixel computer vision Calibration Optical distortion

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