RBF neural network and active circles based algorithm for contours extraction  

作　　者：Zhou Zhiheng Zeng Delu Xie Shengli 

机构地区：[1]College of Electronic ＆ Information Engineering, South China University of Technology, Guangzhou 510640, China

出　　处：《Progress in Natural Science:Materials International》2007年第6期681-686,共6页自然科学进展·国际材料（英文版）

基　　金：Supported by National Natural Science Foundation of China (Grant Nos 60325310 , U0635001);the Science Foundation of Guangdong Provincefor Program Research Team(Grant No 04205783);the Specialized Prophasic Basic Research Projects of Ministry of Science and Technology ,China(Grant No 2005CCA04100);China Postdoctoral Science Foundation (Grant No 20060390728)

摘　　要：For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the contour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels’ intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly.For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the con- tour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels＇ intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly.

关 键 词：contours extraction RBF neural network dynamic clustering 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]