利用Lotka-Volterra递归神经网络的竞争层模型提取长轮廓

Bochuan Zheng, Zhang Yi
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引用次数: 1

摘要

众所周知,当一系列边缘元素以共线或共圆的方式排列时,图像中的轮廓是显著的。利用Lotka-Volterra递归神经网络实现的竞争层模式,构造轮廓提取器提取轮廓。该提取器可以将属于一个轮廓的所有边缘元素绑定到一个层上。为了提取长轮廓,通过沿长轮廓移动所建立的轮廓提取器,可以逐段提取长轮廓。实验表明,该方法能较好地提取图像中的长轮廓。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extracting long contour by using the competitive layer model of the Lotka-Volterra recurrent neural networks
It is well known that contours in image are salient when a series of edge elements are aligned in a collinear or co-circular fashion. In this paper, a contour extractor is constructed to extract contours by using the competitive layer mode implemented by Lotka-Volterra recurrent neural networks. This extractor can bind all edge elements which belong to a contour onto a layer. In order to extract long contours, by moving the established contour extractor along a long contour, then the long contour can be extracted segment by segment. Experiments show that the proposed method can extract long contour properly from images.
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