This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are s...
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This paper proposes an efficient clustering algorithm for region merging. To speed up the search of the best pair of regions which is merged into one region, dissimilarity values of all possible pairs of regions are stored in a heap. Then the best pair can be found as the element of the root node of the binary tree corresponding to the heap. Since only adjacent pairs of regions are possible to be merged in image segmentation, this constraints of neighboring relations are represented by sorted linked lists. Then we can reduce the computation for updating the dissimilarity values and neighboring relations which are influenced by the merging of the best pair. The proposed algorithm is applied to the segmentations of a monochrome image and range images.
When performing a clustering analysis in large data sets using a coefficient based-clustering technique, there may be parts that are so dissimilar that they do not join any part or part cluster except at very low thre...
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When performing a clustering analysis in large data sets using a coefficient based-clustering technique, there may be parts that are so dissimilar that they do not join any part or part cluster except at very low threshold values. Therefore, at threshold values where workable number of part families are obtained, an unbalanced configuration in terms of the number of parts per family can be created. As a result, many part families will have a low number of parts and few families will be formed by a high number of parts. This issue leads to the definition of the term ''remainder cluster'' which is used to designate those parts or cluster of parts that at a certain threshold value do not join any part family and do not have sufficient parts to be considered a feasible part family. In this paper, an approach to deal with the ''remainder clusters'' is proposed. The objective is to form part families, each with a feasible number of parts. (C) 1997 Elsevier Science Ltd parts.
In this paper we propose fuzzy clustering algorithms for directional data. It is a new type of classification maximum likelihood procedure for mixtures of von Mises distributions. These iterative clustering algorithms...
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In this paper we propose fuzzy clustering algorithms for directional data. It is a new type of classification maximum likelihood procedure for mixtures of von Mises distributions. These iterative clustering algorithms give us a new method for analysis of grouped directional data in the plane. The procedure which embeds the fuzzy c-partitions in the model of mixtures of von Mises regressions is derived. This is used as analysis of mixtures of directional regression models. Some numerical examples are given. (C) 1997 Published by Elsevier Science B.V.
The success achieved for protein structure prediction of loop regions with insertions and deletions by knowledge-based methods depends on the quality of the underlying information, i.e. a fragment data bank as complet...
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The success achieved for protein structure prediction of loop regions with insertions and deletions by knowledge-based methods depends on the quality of the underlying information, i.e. a fragment data bank as complete as possible is needed, However the greater the number of proteins contributing to the data base the more redundant information is included, which leads to structurally similar proposals in loop predictions and to longer times for extracting fragments. So it is not only necessary to increase the number of proteins for building the loop data base but also to cluster the resulting fragments according to their structural similarities in order to remove redundancy, Here, a new non-redundant fragment data bank is described, which is based on all proteins in the Brookhaven Protein Data Bank (release 7/95) with a resolution greater than or equal to 2.0 Angstrom and which can be updated easily by including new information from structures to be solved in the future, In the clustering process presented, the resulting clusters are optimized in several cycles until self-consistency. In this way all redundant information is removed without loosing any significantly different fragments, Finally the resulting fragment data bank is analysed with respect to its completeness.
In this paper, a new clustering algorithm is presented to realize clustering with an unknown number of classes and with an unknown or complicated distribution of classes When the number of classes is known, this algor...
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ISBN:
(纸本)7505338900
In this paper, a new clustering algorithm is presented to realize clustering with an unknown number of classes and with an unknown or complicated distribution of classes When the number of classes is known, this algorithm is also effective and has a good adaptability for various distributions of classes.
This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate i...
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This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.
This paper presents a chain vector-quantization clustering (CVQC) algorithm for realtime speech recognition. In comparison with the conventional dynamic time warping (DTW) and vector-quantization (VQ) methods, it deli...
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This paper presents a chain vector-quantization clustering (CVQC) algorithm for realtime speech recognition. In comparison with the conventional dynamic time warping (DTW) and vector-quantization (VQ) methods, it delivers faster training and recognition speeds and requires smaller memory locations.
The problem of reliable automatic target recognition (ATR) from incoherent radar returns is discussed. In the problems under consideration, feature extraction methods are divided into two basic types: (1) Feature extr...
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ISBN:
(纸本)0780326679
The problem of reliable automatic target recognition (ATR) from incoherent radar returns is discussed. In the problems under consideration, feature extraction methods are divided into two basic types: (1) Feature extraction directly based on time-domain description; (2) Feature extraction based on multiple transformation technique. In this paper, we shall demonstrate the problem of feature extraction by considering two examples of ship target recognition. The main algorithms and the recognition processing are discussed in detail. The experimental results show that a high reliability of recognition can be achieved.
This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Re...
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This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stock-keeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.
This paper describes a solution to the following problem: Given a set of weighted data points, find the cluster center points, which minimize the least squared errors. The k-means-type methods produce good results, bu...
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ISBN:
(纸本)9783540548911
This paper describes a solution to the following problem: Given a set of weighted data points, find the cluster center points, which minimize the least squared errors. The k-means-type methods produce good results, but usually the quality of the representation depends on an initial cluster configuration. Also this does not allow a variable number of clusters for a given error tolerance. The proposed method removes these disadvantages by an adaptive sequential insertion of new clusters in those areas, where the largest errors occur. This can be done more efficiently by using multidimensional Voronoi diagrams and local procedures. The data points can be weighted and arbitrarily distributed in the Euclidean space. The weight of each point may be chosen by the user depending on the importance or correctness of that point. At the same time the method produces a hierarchical multidimensional triangulation of the data at different levels of accuracy.
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