A new robust matching method is proposed. The Progressive Sample Consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative ...
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ISBN:
(纸本)0769523722
A new robust matching method is proposed. The Progressive Sample Consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on wide-baseline matching problems.
There are at least two situations in practical computervision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and...
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ISBN:
(纸本)0818672587
There are at least two situations in practical computervision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and the second is when a limited autofocal camera zooms. Motion and zooming are two popular strategies for acquiring more detail or for acquiring depth. The defocus blur has been considered noise or at best been ignored. However, the defocus blur is in itself a cue to depth, and hence we proceed to show how it can be calculated simultaneously with affine motion. We first introduce the theory, then develop a solution method and finally demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
We present an algebraic approach to mullibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial e...
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ISBN:
(纸本)0769523722
We present an algebraic approach to mullibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constrain of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in cu...
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ISBN:
(纸本)0780342364
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced Given a library of models, objects that are similar based an this multi-scale measure are grouped together into classes. Thtr objects that are in the same class ave combined into a single prototype object. Finally the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive nations of scale and approximate similarity for 3-D shapes.
Combination of Multiple Classifiers (CMC) has recently drawn attention as a method of improving classification accuracy. This paper presents a method for combining classifiers that uses estimates of each individual cl...
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ISBN:
(纸本)0818672587
Combination of Multiple Classifiers (CMC) has recently drawn attention as a method of improving classification accuracy. This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. Only the output of the most locally accurate classifier is considered. We address issues of 1) optimization of individual classifiers, and 2) the effect of varying the sensitivity of the individual classifiers on the CMC algorithm. Our algorithm performs better on data from a real problem in mammogram image analysis than do other recently proposed CMC techniques.
We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework based o...
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ISBN:
(纸本)0780342364
We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework based on statistical ANOVA bests to compare and contrast three edge based organization modules, namely those of Etemadi et al. [1], Jacobs [2], and Sarkar-Boyer [3] in the domain of aerial objects using 50 images. With adapted parameters, the Jacobs module is overall the best choice for constraint based recognition. For fixed parameters, the Sarkar-Boyer module is the best In terms of recognition accuracy and indexing speedup. Etemadi et al.'s module performs equally well with fixed and adapted parameters while the Jacobs module is most sensitive to fled and adapted parameter choices. The overall performance ranking of the modules is Jacobs, Sarkar-Boyer, and Etemadi et al..
Advances in computer processing power and emerging algorithms are allowing new ways of envisioning Human computer Interaction. This paper focuses on the development of a computing algorithm that uses audio and visual ...
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Several vision problems can be reduced to the problem of fitting a linear surface of low dimension to data, including the problems of structure-from-affine-motion, and of characterizing the intensity images of a Lambe...
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ISBN:
(纸本)0780342364
Several vision problems can be reduced to the problem of fitting a linear surface of low dimension to data, including the problems of structure-from-affine-motion, and of characterizing the intensity images of a Lambertian scene by constructing the intensity manifold. For these problems, one must deal with a data matrix with some missing elements. In structure-from-motion, missing elements will occur if some point features are not visible in some frames. To construct the intensity manifold missing matrix elements will arise when the surface normals of some scene points do not face the light source in some images. We propose a novel method for fitting a low rank matrix to a matrix with missing elements. We show experimentally that our method produces good results in the presence of noise. These results can be either used directly, or can serve as an excellent starting point for an iterative method.
The vast majority of corner and edge detectors measure image intensity gradients in order to estimate the positions and strengths of features. However, many of the most popular intensity gradient estimators are inhere...
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ISBN:
(纸本)0818672587
The vast majority of corner and edge detectors measure image intensity gradients in order to estimate the positions and strengths of features. However, many of the most popular intensity gradient estimators are inherently and significantly anisotropic. In spite of this, few algorithms take the anisotropy into account, and so the set of features uncovered is typically sensitive to rotations of the image, compromising recognition, matching (e.g. stereo), and tracking. We introduce an effective technique for removing unwanted anisotropies from analytical gradient estimates, by measuring local intensity gradients in four directions rather than the more traditional two. In experiments using real image data, our algorithm reduces the gradient anisotropy associated with conventional analytical gradient estimates by up to 85%, yielding more consistent feature topologies.
Tire's paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of gro...
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ISBN:
(纸本)0780342364
Tire's paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. Body plans can be learned from image data, using established statistical learning techniques. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand;We other involves determining whether a picture contains a horse, using a body plan learned front image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.
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