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Hypergraph imaging: an overview
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DOI：10.1016/S0031-3203(01)00067-X.png)
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摘要
En 中文
Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Pat-is, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting For many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation, (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
combinatorial optimization
image processing
image model
segmentation
edge detection
noise reduction
hypergraph
graph
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7.6
论文数:
1.3W
被引数:
4.5W
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