A method based on the properties of orientation fields is presented for the estimation of a set of symbolic descriptors from nondegenerate linear orientation fields, modeled by two-dimensional first-order phase portra...
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A method of obtaining local projective and affine invariants that is more robust than existing methods is presented. These shape descriptors are useful for object recognition because they eliminate the search for the ...
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A novel method of robust skeletonization based on the Voronoi diagram of boundary points, which is characterized by correct Euclidean metries and inherent preservation of connectivity, is presented. The regularization...
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Previous work [5], [2] have developed an approach for estimating shape and albedo from multiple images assuming Lambertian reflectance with single light sources. The main contributions of this paper are: (i) to show h...
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ISBN:
(纸本)0780342364
Previous work [5], [2] have developed an approach for estimating shape and albedo from multiple images assuming Lambertian reflectance with single light sources. The main contributions of this paper are: (i) to show how the approach can be generalized to include ambient background illumination, (ii) to demonstrate the use of the integrability constraint for solving this problem, and (iii) an iterative algorithm which is able to improve the analysis by finding shadows and rejecting them.
The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computervision. Image data, however, is usually also corrupted by a z...
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Current methods for registering image regions perform well for simple transformations or large image regions. In this paper, we present a new method that is better able to handle small image regions as they deform wit...
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ISBN:
(纸本)0780342364
Current methods for registering image regions perform well for simple transformations or large image regions. In this paper, we present a new method that is better able to handle small image regions as they deform with non-linear transformations. We introduce difference decompositon, a novel approach to solving the registration problem. The method is a generalization of previous methods and can better handle non-linear transforms. Although the methods are general, we focus on projective transformations and introduce piecewise-projective transformations for modeling the motions of non-planar objects. We conclude with examples from our prototype implementation.
We develop a simple and very fast method for object tracking based exclusively on color information in digitized video images. Running on a Silicon Graphics R4600 Indy system with an IndyCam, our algorithm is capable ...
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ISBN:
(纸本)0780342364
We develop a simple and very fast method for object tracking based exclusively on color information in digitized video images. Running on a Silicon Graphics R4600 Indy system with an IndyCam, our algorithm is capable of simultaneously tracking objects at full frame size (640 x 480 pixels) and video frame rate (30 fps). Robustness with respect to occlusion is achieved via an explicit hypothesis-tree model of the occlusion process. We demonstrate the efficacy of our technique in the challenging task of tracking people, especially tracking human heads and hands.
A multiscale filtering scheme based on the three Matheron axioms for morphological openings is developed. It is shown that opening a signal with a gray scale operator does not introduce additional zero-crossings as on...
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It is shown how random perturbation models can be set up for a vision algorithm sequence involving edge finding, edge linking, and gap filling. By starting with an appropriate noise model for the input data, the autho...
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In the depth from defocus (DFD) method two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed...
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ISBN:
(纸本)0780342364
In the depth from defocus (DFD) method two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the DFD problem in a maximum likelihood-based framework. We propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. Simulations as well as experimental results on real images are presented for validation.
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