Presents an efficient method which uses two neighboring frames in image sequences for target identification. Using statistical information about the background noise and candidate regions' noise after background r...
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ISBN:
(纸本)0780342534
Presents an efficient method which uses two neighboring frames in image sequences for target identification. Using statistical information about the background noise and candidate regions' noise after background registration, we can determine those candidate regions that have the same or similar noise distributions to the background's which should be background regions, and those candidate regions that have different noise distributions from the background's which should be the target region. In particular, when there is only one target in the image, we can simply determine that the candidate region whose noise distribution is most different from the background's is the true target.
Matching confidence is an important element for evaluating image matching quality. A method is presented which uses the technique of tests of hypotheses to determine image matching confidence under a certain testing l...
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ISBN:
(纸本)0780340531
Matching confidence is an important element for evaluating image matching quality. A method is presented which uses the technique of tests of hypotheses to determine image matching confidence under a certain testing level. The authors use similarity measurement values to be the statistics. Experimental results with large real images prove the effectiveness of the method to determine image matching confidence.
Reasoning about action is an important aspect of common sense reasoning and planning. It gives rise to three classical problems: the frame problem,the qualification problem and the ramification problem. Ekisting appro...
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Reasoning about action is an important aspect of common sense reasoning and planning. It gives rise to three classical problems: the frame problem,the qualification problem and the ramification problem. Ekisting approaches cannot deal with these problems efficiently. This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.
Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordin...
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Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordinate system and a Fourier expansion.
A method is presented for computing the pose of rigid 3D objects with arbitrary curved surfaces. Given an input image and a candidate object model and aspect, the method will verify whether or not the object is presen...
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A method is presented for computing the pose of rigid 3D objects with arbitrary curved surfaces. Given an input image and a candidate object model and aspect, the method will verify whether or not the object is present acid if so, report pose parameters. The curvature method of Basri and Ullman is used to model points on the object rim, while stereo matching is used for internal edge points. The model allows an object edgemap to be predicted from pose parameters. Pose is computed via an iterative search for the best pose parameters. Heuristics are used so that matching can succeed in the presence of occlusion and artifact and without resorting to use of corresponding salient feature points. Bench tests and simulations show that the method almost always converges to ground truth pose parameters for a variety of objects and for a broad set of starting parameters in the same aspect.
In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with nonmonotonic reasoning,...
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In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with nonmonotonic reasoning, so the ATMS-based EBL system can improve the efficiency of explanation-based learning, deal with multiple explanation problems in learning from imperfect theories by prioritized reasoning and multiple example verification and can give biases for induction in integrated learning. Copyright (C) 1996 Elsevier Science Ltd
This paper presents an approach to text recognition which avoids the problems of thresholding and segmentation by working directly on the grey-level image recognizing an entire word at the time. For each word a sequen...
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This paper presents an approach to text recognition which avoids the problems of thresholding and segmentation by working directly on the grey-level image recognizing an entire word at the time. For each word a sequence of grey-level feature vectors is extracted. Hidden Markov models are used to describe the single characters and the sequence of feature vectors is matched against all possible combinations of models using dynamic programming. (C) 1996 patternrecognition Society. Published by Elsevier Science Ltd.
Since the mid-1970s, a large number of visual inspection systems and algorithms for industrial inspection have been developed. To be acceptable in industry, vision systems must be inexpensive, within the speed of the ...
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We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is m...
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We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptivel...
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We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the centers of the basis functions and trains the linear weights. The selection procedure performs the elimination of some of the basis functions using an objective function. By iteratively combining these two procedures we achieve a controlled way of training and modifying RBF networks, which balances accuracy, learning time, and complexity of the resulting network.
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