基于迟滞连接与预测的边缘检测方法

Shao-qing Mo, Haiyun Gan, Rui Zhang, Ying Yan, Xiaofeng Liu
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引用次数: 1

摘要

边缘检测是许多视觉系统的关键组成部分,包括目标检测器和图像分割算法。在分析边缘梯度变化和传统边缘检测算法缺陷的基础上,提出了一种新的边缘检测方法。首先,用给定阈值的梯度映射二值化方法初始化边缘集;然后,如果边缘像素的梯度大于阈值的一半,或者该像素与相邻强边缘的梯度差小于给定值,则将边缘像素相邻的像素加入边缘集。该方法利用边缘的局部信息预测边缘的扩展方向,并在该方向上搜索不连续的弱边。实验结果表明,该方法不仅可以提取完整的边缘段,而且可以在破碎区间内检测出有效的弱边缘段,同时可以抑制具有相同梯度值的噪声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Detection Method Based on Hysteresis Connection and Prediction
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Based on the analysis of edge gradient changes and the defects of traditional edge detection algorithms, a novel edge detection method is proposed. Firstly, the edge set is initialized by gradient map binaryzation with a given threshold. Then the pixel adjacent to edge pixel will be joined to the edge set, if its gradient is higher than half of threshold or the gradient difference between this pixel and the adjacent strong edge is less than a given value. In order to go over the broken interval, this method predicts the edge extension direction using the local information of edge, and searches for discontinuous weak edges on this direction. The experimental results show that this method not only can extract complete edge segments, but also can detect the valid weak edge segment over the broken interval, and at the same time it can suppress the noise with the same gradient value.
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