Single level set based fast images segmentation model with multiple regions

Honghui Zhang; Yong Tan; Shanshan Li

doi:10.1117/12.748925

15 November 2007 Single level set based fast images segmentation model with multiple regions

Honghui Zhang, Yong Tan, Shanshan Li

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67861T (2007) https://doi.org/10.1117/12.748925
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

In traditional image segmentation models based on single level set function, only two regions can be identified because different regions are identified by the signs of single level set function. Though several segmentation models with multiple regions have been proposed, but the largest number of regions that can be identified was limited by the number of embedded level set functions in them. Moreover, the more embedded level set functions, the higher the time cost, usually increasing linearly with the increase of embedded level set functions. In this paper, by introducing the segmentation-measure function, a new model for multi-regions image segmentation based on single level set function is proposed. At the same time, a new initialization function for the level set function is also proposed in order to reduce the time cost of the segmentation model. The experiment results show that the new Image segmentation mode with multiple regions proposed in this paper performs well and dramatically reduces the time cost compared with the popular model for multiple regions proposed by Vese and Chan.

Citation Download Citation

Honghui Zhang, Yong Tan, and Shanshan Li "Single level set based fast images segmentation model with multiple regions", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67861T (15 November 2007); https://doi.org/10.1117/12.748925

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KEYWORDS

Image segmentation

Image processing

Lithium

Performance modeling

Computer vision technology

Electronics engineering

Information science

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