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Vector bundle constraint for particle swarm optimization and its application to active contour modeling

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Progress in Natural Science:Materials International 2007年第10期17卷 1220-1225页

作者： zeng delu, zhou zhiheng and xie shengli(College of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China) College of Electronic and Information Engineering South China University of Technology Guangzhou 510641 China

Active contour modeling (ACM) has been shown to be a powerful method in object boundary extraction. In this paper, a new ACM based on vector bundle constraint for particle swarm optimization (VBCPSO-ACM) is proposed. Different from the traditional particle swarm optimization (PSO), in the process of velocity update, a vector bundle is predefined for each particle and velocity update of the particle is restricted to its bundle. Applying this idea to ACM, control points on the contour are treated as particles in PSO and the evolution of the contour is driven by the particles. Meanwhile, global searching is shifted to local searching in ACM by decreasing the number of neighbors and inertia. In addition, the addition and deletion of particles on the active contour make this new model possible for representing the real boundaries more precisely. The proposed VBCPSO-ACM can avoid self-intersection during contour evolving and also extract inhomogeneous boundaries. The simulation results proved its great performance in performing contour extraction.

关键词： boundary extraction active contour modeling particle swarm optimization velocity update vector bundle.

RBF neural network and active circles based algorithm for contours extraction

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Progress in Natural Science:Materials International 2007年第6期17卷 681-686页

作者： zhou zhiheng, zeng delu and xie shengli(College of Electronic & Information Engineering, South China University of Technology, Guangzhou 510640, China) College of Electronic and Information Engineering South China University of Technology Guangzhou 510640 China

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.

关键词： contours extraction RBF neural network dynamic clustering.

Global minimization of adaptive local image fitting energy for image segmentation

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Journal of Systems Engineering and Electronics 2014年第2期25卷 307-313页

作者： Guoqi Liu zhiheng zhou shengli xie School of Electronic and Information Engineering South China University of Technology School of Automation Guangdong University of Technology

The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.

关键词： image segmentation level set adaptive local imagefitting (LIF) energy.

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基于电流波数扫描的透镜三维轮廓信息测量

机电工程技术 2019年第12期48卷 115-117页

作者：全绍琛曾丹平周延周谢胜利广东工业大学自动化学院

为了高精度测量透镜表面的三维轮廓信息,采用半导体激光器波数扫描的方法,通过控制激光的输出电流来控制激光的线性输出并扩大线性区,从而提高测量精度。对激光波数扫描系统进行了理论分析并建立了测量系统进行实验验证。在迈克尔逊干... 详细信息

为了高精度测量透镜表面的三维轮廓信息,采用半导体激光器波数扫描的方法,通过控制激光的输出电流来控制激光的线性输出并扩大线性区,从而提高测量精度。对激光波数扫描系统进行了理论分析并建立了测量系统进行实验验证。在迈克尔逊干涉系统的基础上进行改进,在参考端引入1个参考光楔,通过2-D傅里叶变换可以在线监测输出波数变化,对干涉序列图像进行随机采样傅里叶变换,从而还原透镜表面三维轮廓。实验结果表明测量精度可达到20 nm。

关键词：电流控制波数扫描激光技术三维轮廓测量随机傅里叶采样

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