8 February 2017 SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022518 (2017) https://doi.org/10.1117/12.2266227
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
Abstract
In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.
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Xiaomin Xie, Xiaomin Xie, Tingting Wang, Tingting Wang, } "SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022518 (8 February 2017); doi: 10.1117/12.2266227; https://doi.org/10.1117/12.2266227
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