15 November 2007 Multi-object segmentation algorithm based on improved Chan-Vese model
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Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67863W (2007) https://doi.org/10.1117/12.750643
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Because Chan and Vese(C-V) model using one level set function can only represent one object and one background, it cannot represent multiple junctions of multiple objects. In this paper, an improved multi-object segment algorithm is proposed based on C-V model of single level set. First, the given image resolution is deduced by wavelet transform. Since the low resolution approximate image contains less noise and pixels, it can speed up the active contour evolution. Secondly, an improved C-V model of a single level set is introduced to obtain the multi-objects' approximate contour, which can make use of topology split information of the contour effectively. Thirdly, the inverse discrete wavelet transform is used to the resulted image and level set of the coarse scale image, which can get the approximation contour on the original image. Lastly, the approximation contour is taken as an initial level set function and the second active contour evolution is performed on the original image to get the real multi-objects contour. Experimental results show that the proposed algorithm can realize the multi-object segmentation effectively and quickly.
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Xiaowei Fu, Xiaowei Fu, Mingyue Ding, Mingyue Ding, } "Multi-object segmentation algorithm based on improved Chan-Vese model", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863W (15 November 2007); doi: 10.1117/12.750643; https://doi.org/10.1117/12.750643
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