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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28506

Title: Improved Snake Model for Fast Image Segmentation
Authors: Chi-Cheng Ting;Jhan-Syuan Yu;Jiun-Shuen Tzeng;Jung-Hua Wang
Contributors: NTOU:Department of Electrical Engineering
國立臺灣海洋大學:電機工程學系
Date: 2006
Issue Date: 2011-10-21T02:37:50Z
Publisher: Neural Networks, 2006. IJCNN '06. International Joint Conference on
Abstract: Abstract:This paper presents an improved snake model (ISM) effective for performing fast image segmentation. The work takes advantage of two well-known snake models of Balloon (Cohen, 1991) and GGVF (generalized gradient vector flow) (Xu and prince, 1998). The Balloon model is well known for its capability of fast locating object boundary using an extra pressure force field, but it may easily cause the contour to overwhelm the boundary. The more accurate GGVF model, however, requires lengthy computation time due to the need to specify a relatively large range in order to cover all the control points inside the range. Our goal is to obtain the exact field provided by GGVF while at the same time to expand the capture range with the Balloon pressure force. To this end, in ISM the force fields of Balloon and GGVT are integrated and a dynamic scheme for setting the control points is employed to avoid the time-consuming "re-sampling process" in both GVF (gradient vector flow) and GGVF. The key attribute of ISM is that it can reduce the number of training iterations while maintaining the capability of pushing the capture range toward the gradient map. Empirical results show that the proposed model can deliver the same performance level of segmentation using less computation time than "GGVF" does.
Relation: pp.3936-3941
URI: http://ntour.ntou.edu.tw/handle/987654321/28506
Appears in Collections:[電機工程學系] 演講及研討會

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