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3 November 2005 Wavelet-based snake model for image segmentation
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Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604420 (2005)
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Although the snake model has been widely used nowadays and obtained quite good results, there are still some key difficulties with it: the narrow capture range and the disability to move into boundary concavities. A new snake model, Gradient Vector Flow snake, can overcome this difficulty. GVF snake model creates its own external force field called GVF force field, this make it insensitive to the initialization and able to move into concave boundary regions. However, GVF snake need large amount of computation and is easily interfered by noise. Accordingly, the wavelet-based GVF snake model can lessen the amount of computation because the multi-scale character of wavelet transform. Due to the different singularities of signal and noise, the module local maxima of their wavelet coefficients vary in different way in multi resolution, so noise can also be distinguished from signal with wavelet-based GVF snake model. The wavelet-based GVF snake model is more quickly and robust contrast to traditional snake model.
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Hong-wei Zhang and Zheng-guang Liu "Wavelet-based snake model for image segmentation", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604420 (3 November 2005);

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