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A New Approach for Automated Image Segmentation Based on Simplified PCNN
  • 期刊名称:Computer Aided Drafting, Design and Manufacturing
  • 时间:2013
  • 页码:21-26
  • 分类:TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构:[1]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
  • 相关基金:Supported by NSFC (11071270).
  • 相关项目:近景摄影测量中的自动图像分割技术
中文摘要:

Pulse-coupled neural network (PCNN) is a novel neural network,which has been widely used in image segmentation.However,there are still some limitations,such as trial-and-error parameter settings and manual selection of the optimal results.This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation.By calculating the uniformity measure of the corresponding image at each process of iteration,the optimal segmentation result is obtained when the maximum value of the uniformity measure is achieved.Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.

英文摘要:

Pulse-coupled neural network (PCNN) is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.

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