遗传算法设计梯度算子实现优化的图象边界检测
Edge Detection Based on the Gradient Operators Optimized by Genetic Algorithms
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摘要: 计算机图象处理技术可广泛应用各种图象测试、模式识别和计算机视觉等领域,而图象边界检测是计算机图象处理的最基本和最低层的步骤之一.由于噪声的干扰和图象光照不均匀等因素的影响,目前的图象边界检则方法还不能有效地检测出各种不同模式的边界.本文提出了一种基于遗传算法和灰度梯度算子的图象边界检测法,通过对样本图象的训练,设计对训练样本模式最优的灰度梯度算子,增加了对噪声的抗干扰能力,并且使被检测出的边界更准确.Abstract: Computer image processing technology has been widely used in various image test,pattern recognition and computer vision,and edge detection is one of the fundamental steps on the lowest level of image processing.Due to the affection of the noise and unbalanced light source,no method can detect the edges of all modals.There fore, an approach based on gray level's gradient operators and genetic algorithm was proposed in this paper.This approach can optimize the gray level’s gradient operators by genetic algorithm,and the fitness function of the genetic algorithms is the least square error criterion used in the procedure of supervised training.The proposed method can obtain the optimal edge detector for the image modal of training set.The optimized gradient operators present good performance in lower SNR and make the detected edges more precise.