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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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Ridge's corner detection and correspondence

Ridge's corner detection and correspondence

引用

conference on computer vision and pattern recognition (cvpr)

作者： F. Shilat M. Werman Y. Gdalyahn Institute of Computer Science Hebrew University of Jerusalem Jerusalem Israel Hebrew University of Jerusalem Jerusalem Jerusalem IL

Traditionally, corners are found along step edges. In this paper we present an alternative approach-corners along ridges/troughs and local minima points. These features seem to be more reliable for tracking. A new app... 详细信息

关键词： Image edge detection computer vision Layout Calibration computer science Navigation Tensile stress Image sequences Motion estimation Detectors

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Visibility constrained surface evolution

Visibility constrained surface evolution

引用

conference on computer vision and pattern recognition (cvpr)

作者： J.E. Solem F. Kahl A. Heyden Applied Mathematics Group School of Technology and Society Malmö University Sweden Department of Computer Science & Engineering University of California La Jolla CA USA

The problem of feature-based surface reconstruction is considered in this paper. Our main contribution is the ability to handle visibility constraints, obtained from the projections of points, curves and silhouettes, in the surface fitting process. While traditional methods often ignore such information, we show that visibility constraints not only give better initial surface estimates and faster convergence, but also provide an important cue for determining surface topology. The problem is cast as a variational problem with constraints within the level set framework. It is shown how to evolve the surface without violating the visibility constraints using methods from variational calculus. Applications of the theory are detailed for a number of important cases of geometric primitives: points, curves and visual hulls. Several experiments on real image sequences are given to demonstrate the performance of the approach.

关键词： Surface fitting Surface reconstruction Layout Level set Image reconstruction Topology Cameras Clouds Mathematics computer science

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Visual recognition using concurrent and layered parameter networks

Visual recognition using concurrent and layered parameter ne...

引用

conference on computer vision and pattern recognition (cvpr)

作者： R.M. Bolle A. Califano R. Kjeldsen R.W. Taylor IBM Thomas J. Watson Research Center Exploratory Computer Vision Group Yorktown Heights NY USA

A vision system to recognize 3-D objects is presented. A novel notion of generalized feature makes it possible to develop a homogeneous architecture to support recognition from simple partial features to complex feature assemblies and 3-D objects. Layered concurrent parameter transforms vote for feature hypotheses on the basis of image data and previously reconstructed features. recognition networks, motivated by connectionist systems, collect votes, fuse evidence from various sources and ensure global consistency. In addition, they provide an integrated solution to the segmentation problem. The highly modular design allows fundamentally different types of features to interact in a coherent way, utilizing redundancies for more robust recognition. Within this paradigm, a system extracts planar patches, patches of quadrics of revolution, and the intersection curves of these surfaces (lines and conic sections in three-space) from a depth map. Reconstructed features index into a model database to form consistent object hypotheses. Experimental results detailing the recognition behavior or real depth maps are included.< >

关键词： Voting Image reconstruction Machine vision Assembly Fuses Image segmentation Robustness Surface reconstruction Indexes Spatial databases

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Learning discriminant features for multi-view face and eye detection

Learning discriminant features for multi-view face and eye d...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Peng Wang Qiang Ji Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA

In current face detection, mostly often used features are selected from a large set (e.g. Haar wavelets). Generally Haar wavelets only represent the local geometric feature. When applying those features to profile faces and eyes with irregular geometric patterns, the classifier accuracy is low in the later training stages, only near 50%. In this paper, instead of brute-force searching the large feature set, we propose to statistically learn the discriminant features for object detection. Besides applying Fisher discriminant analysis(FDA) in AdaBoost, we further propose the recursive nonparametric discriminant analysis (RNDA) to handle more general cases. Those discriminant analysis features are not constrained with geometric shape and can provide better accuracy. The compact size of feature set allows to select a near optimal subset of features and construct the probabilistic classifiers by greedy searching. The proposed methods are applied to multi-view face and eye detection and achieve good accuracy.

关键词： Face detection Object detection computer vision pattern recognition Feature extraction Statistical learning Systems engineering and theory Eyes Shape Error analysis

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A Multichannel Edge-Weighted Centroidal Voronoi Tessellation algorithm for 3D super-alloy image segmentation

A Multichannel Edge-Weighted Centroidal Voronoi Tessellation...

引用

作者： Cao, Yu Ju, Lili Zou, Qin Qu, Chengzhang Wang, Song Department of Computer Science and Engineering University of South Carolina Columbia SC 29208 United States Department of Mathematics University of South Carolina Columbia SC 29208 United States School of Remote Sensing and Infomation Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781457703942

In material science and engineering, the grain structure inside a super-alloy sample determines its mechanical and physical properties. In this paper, we develop a new Multichannel Edge-Weighted Centroidal Voronoi Tessellation (MCEWCVT) algorithm to automatically segment all the 3D grains from microscopic images of a super-alloy sample. Built upon the classical k-means/CVT algorithm, the proposed algorithm considers both the voxel-intensity similarity within each cluster and the compactness of each cluster. In addition, the same slice of a super-alloy sample can produce multiple images with different grain appearances using different settings of the microscope. We call this multichannel imaging and in this paper, we further adapt the proposed segmentation algorithm to handle such multichannel images to achieve higher grain-segmentation accuracy. We test the proposed MCEWCVT algorithm on a 4-channel Ni-based 3D super-alloy image consisting of 170 slices. The segmentation performance is evaluated against the manually annotated ground-truth segmentation and quantitatively compared with other six image segmentation/edge-detection methods. The experimental results demonstrate the higher accuracy of the proposed algorithm than the comparison methods. © 2011 ieee.

关键词： Image segmentation

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Fusion-Based Background-Subtraction using Contour Saliency

Fusion-Based Background-Subtraction using Contour Saliency

引用

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

作者： J.W. Davis V. Sharma Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present a new contour-based background-subtraction technique using thermal and visible imagery for persistent object detection in urban settings. Statistical backgroundsubtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regionsof- interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then combined. An A path-constrained search along watershed boundaries is used to complete and close any broken contour segments. Lastly, the contour image is flood- filled to produce silhouettes. Results of our approach are presented and compared against manually segmented data.

关键词： Object detection Cameras Layout Thermal sensors Image segmentation Lighting Temperature sensors computer vision computer science Thermal engineering

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Matching Tensors for Pose Invariant Automatic 3D Face recognition

Matching Tensors for Pose Invariant Automatic 3D Face Recogn...

引用

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

作者： A.S. Mian M. Bennamoun R.A. Owens School of Computer Science and Software Engineering University of Western Australia Crawley WA Australia

The face is an easily collectible and non-intrusive biometric used for the authentication and identification of individuals. 2D face recognition techniques are sensitive to changes in illumination, makeup and pose. We present a fully automatic 3D face recognition algorithm that overcomes these limitations. During the enrollment, 3D faces in the gallery are represented by third order tensors which are indexed by a 4D hash table. During online recognition, tensors are computed for a probe and are used to cast votes to the tensors in the gallery using the hash table. Gallery faces are ranked according to their votes and a similarity measure based on a linear correlation coefficient and registration error is calculated only for the high ranked faces. The face with the highest similarity is declared as the recognized face. Experiments were performed on a database of 277 subjects and a rank one recognition rate of 86.4% was achieved. Our results also show that our algorithm’s execution time is insensitive to the gallery size.

关键词： Tensile stress Face recognition Probes Biometrics Voting Databases Authentication Lighting computer science Software engineering

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Multi-view geometry for general camera models

Multi-view geometry for general camera models

引用

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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Radiometric Compensation in a Projector-Camera System Based Properties of Human vision System

Radiometric Compensation in a Projector-Camera System Based ...

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

作者： Dong Wang I. Sato T. Okabe Y. Sato Institute of Industrial Science University of Tokyo Tokyo Japan

We introduce a novel technique for performing radiometric compensation for a projector-camera system that projects images onto a textured planar surface, which is designed to minimize perceptual artifacts visible to observers according to a model of the human vision system. A projector-camera system has previously been proposed for projecting images onto an arbitrary surface using radiometric compensation, however the dynamic range of a projector is physically limited there are some textures that cannot be compensated correctly. Also, human eyes are sensitive to artifacts introduced in this way. Our technique is designed to provide compensated images with perceptually less noticeable artifacts while preserving enough brightness and contrast in the output. We develop an optimization framework based on a perceptually-based physical error metric to minimize perceptible artifacts in the final compensated images by compressing the contrast of the input images.

关键词： Radiometry Humans Machine vision Dynamic range Brightness Displays Surface texture Image coding Eyes Photometry

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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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