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An invariant, closed-form solution for matching sets of 3D lines

An invariant, closed-form solution for matching sets of 3D l...

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conference on computer vision and pattern recognition (cvpr)

作者： B. Kamgar-Parsi Be. Kamgar-Parsi Office of Naval Research Arlington VA USA Navy Center for Applied Research in Al Naval Research Laboratory Inc. Washington D.C. USA

Existing algorithms for finding the best match between two sets of 3D lines are not completely satisfactory in the sense that they either yield approximate solutions, or are iterative which means they may not converge to the globally optimal solution. An even more serious shortcoming of the existing algorithms is that they are all non-invariant with respect to the translation of the coordinate system. Thus, any best match found becomes rather meaningless. In this paper, we discuss the source of this non-invariance and present a new algorithm that is invariant to coordinate transforms. Moreover, the algorithm is closed-form, which implies that it always yields the best global match.

关键词： Closed-form solution Iterative algorithms Image segmentation Motion estimation Image converters Face detection computer vision Application software Layout Face recognition

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PCA-SIFT: a more distinctive representation for local image descriptors

PCA-SIFT: a more distinctive representation for local image ...

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conference on computer vision and pattern recognition (cvpr)

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

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.

关键词： Image retrieval computer vision Object recognition Principal component analysis Filters computer science Object detection Image registration Histograms Robustness

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Sharing features: efficient boosting procedures for multiclass object detection

Sharing features: efficient boosting procedures for multicla...

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conference on computer vision and pattern recognition (cvpr)

作者： A. Torralba K.P. Murphy W.T. Freeman MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA

We consider the problem of detecting a large number of different object classes in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, which can be slow and require much training data. We present a multi-class boosting procedure (joint boosting) that reduces both the computational and sample complexity, by finding common features that can be shared across the classes. The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required is observed to scale approximately logarithmically with the number of classes. In addition, we find that the features selected by independently trained classifiers are often specific to the class, whereas the features selected by the jointly trained classifiers are more generic features, such as lines and edges.

关键词： Boosting Object detection Layout computer science Artificial intelligence Batteries Training data Detectors Machine vision Machine learning

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Feature selection for classifying high-dimensional numerical data

Feature selection for classifying high-dimensional numerical...

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conference on computer vision and pattern recognition (cvpr)

作者： Yimin Wu Aidong Zhang Department of Computer Science and Engineering Suny at Buffalo USA

Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffers from the curse of dimensionality, which degrades both classification accuracy and efficiency. To address this issue, we present an efficient feature selection method to facilitate classifying high-dimensional numerical data. Our method employs balanced information gain to measure the contribution of each feature (for data classification); and it calculates feature correlation with a novel extension of balanced information gain. By integrating feature contribution and correlation, our feature selection approach uses a forward sequential selection algorithm to select uncorrelated features with large balanced information gain. Extensive experiments have been carried out on image and gene microarray datasets to demonstrate the effectiveness and robustness of the presented method.

关键词： Feedback Supervised learning Training data Filters Information retrieval Gain measurement pattern recognition Multimedia systems Bioinformatics Uncertainty

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Scale-invariant shape features for recognition of object categories

Scale-invariant shape features for recognition of object cat...

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conference on computer vision and pattern recognition (cvpr)

作者： F. Jurie C. Schmid GRAVIR INRIA-CNRS Montbonnot Saint Martin France

We introduce a new class of distinguished regions based on detecting the most salient convex local arrangements of contours in the image. The regions are used in a similar way to the local interest points extracted from gray-level images, but they capture shape rather than texture. Local convexity is characterized by measuring the extent to which the detected image contours support circle or arc-like local structures at each position and scale in the image. Our saliency measure combines two cost functions defined on the tangential edges near the circle: a tangential-gradient energy term, and an entropy term that ensures local support from a wide range of angular positions around the circle. The detected regions are invariant to scale changes and rotations, and robust against clutter, occlusions and spurious edge detections. Experimental results show very good performance for both shape matching and recognition of object categories.

关键词： Shape Image edge detection Robustness computer vision Position measurement Entropy Energy measurement Cost function Character recognition Object recognition

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Computing depth under ambient illumination using multi-shuttered light

Computing depth under ambient illumination using multi-shutt...

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conference on computer vision and pattern recognition (cvpr)

作者： H. Gonzalez-Banos J. Davis Honda Research Institute Mountain View CA USA

Range imaging has become a critical component of many computer vision applications. The quality of the depth data is of critical importance, but so is the need for speed. Shuttered light-pulse (SLP) imaging uses active illumination hardware to provide high quality depth maps at video frame rates. Unfortunately, current analytical models for deriving depth from SLP imagers are specific to the number of shutters and have a number of deficiencies. As a result, depth estimation often suffers from bias due to object reflectivity, incorrect shutter settings, or strong ambient illumination such as that encountered outdoors. These limitations make SLP imaging unsuitable for many applications requiring stable depth readings. This paper introduces a method that is general to any number of shutters. Using three shutters, the new method produces invariant estimates under changes in ambient illumination, producing high quality depth maps in a wider range of situations.

关键词： Lighting Reflectivity computer vision Hardware Application software Analytical models High-resolution imaging Pulse measurements Surveillance Face recognition

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Segmenting, modeling, and matching video clips containing multiple moving objects

Segmenting, modeling, and matching video clips containing mu...

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conference on computer vision and pattern recognition (cvpr)

作者： F. Rothganger S. Lazebnik C. Schmid J. Ponce Department of Computer Science and Beckman Institute University of Illinois Urbana-Champaign Urbana-Champaign IL USA INRIA Rhône-Alpes Montbonnot Saint Martin France

This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and be observed by a moving camera. Multi-view constraints associated with groups of affine-invariant scene patches and a normalized description of their appearance are used to segment a scene into its rigid parts, construct three-dimensional protective, affine, and Euclidean models of these parts, and match instances of models recovered from different image sequences. The proposed approach has been implemented, and it is applied to the detection and recognition of moving objects in video sequences and the identification of shots that depict the same scene in a video clip (shot matching).

关键词： Layout Image segmentation Shape Image sequences Indexing computer science Cameras Object detection Gunshot detection systems Video sequences

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Separating reflections from a single image using local features

Separating reflections from a single image using local featu...

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conference on computer vision and pattern recognition (cvpr)

作者： A. Levin A. Zomet Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

When we take a picture through a window, the image we obtain is often a linear superposition of two images: the image of the scene beyond the window plus the image of the scene reflected by the window. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there is an infinite number of valid decompositions. We describe an algorithm that uses an extremely simple form of prior knowledge to perform the decomposition. Given a single image as input, the algorithm searches for a decomposition into two images that minimize the total amount of edges and comers. The search is performed using belief propagation on a patch representation of the image. We show that this simple prior is surprisingly powerful: our algorithm obtains "correct" separations on challenging reflection scenes using only a single image.

关键词： Reflection Layout Reflectivity computer vision Equations Filters Motion pictures Image sequence analysis Statistics Cost function

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Generalized quotient image

Generalized quotient image

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conference on computer vision and pattern recognition (cvpr)

作者： Haitao Wang S.Z. Li Yangsheng Wang Institute of Automation Chinese Academy and Sciences Beijing China Beijing Sigma Center Microsoft Research Asia Beijing China

We present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI, spherical harmonic, image ratio and retinex. Under this framework, we generalize these previous works into two new algorithms: (1) non-point light quotient image (NPL-QI) extends QI to deal with non-point light sources by modeling non-point light directions using spherical harmonic bases; (2) self-quotient image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.

关键词： Lighting Face recognition Light sources Power harmonic filters Image recognition Shape Face detection Surface texture Automation Asia

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Motion layer extraction in the presence of occlusion using graph cut

Motion layer extraction in the presence of occlusion using g...

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conference on computer vision and pattern recognition (cvpr)

作者： Jiangjian Xiao M. Shah Computer Vision Lab School of Computer Science University of Central Florida Orlando FL USA

Extracting layers from video is very important for video representation, analysis, compression, and recognition. Assuming that a scene can be approximately described by multiple planar regions, this paper describes a robust novel approach to automatically extract a set of affine transformations induced by these regions, and accurately segment the scene into several motion layers. First, a number of seed regions are determined by using two frame correspondences. Then the seed regions are expanded and refined using the level set representation and employing graph cut method. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, after exploiting the occlusion order constraint on multiple frames the robust layer extraction is obtained by graph cut algorithm, and the occlusions between the overlapping layers are explicitly determined. Several examples are demonstrated in the experiments to show that our approach is effective and robust.

关键词： computer vision Motion segmentation Pixel Video sequences Video compression Layout Robustness Motion estimation Image sequences Maximum likelihood estimation

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