1 内容介绍
边缘信息是图像最基本的特征,所包含的也是图像中用于识别的有用信息。为人们描述或识别目标以及解释图像提供了有价值的和重要的信息。边缘检测一直是计算机视觉和图像处理领域的经典研究课题之一。其目的是去发现图像中关于形状和反射或透射比的信息,是图像处理、图像分析、模式识别、计算机视觉以及人类视觉的基本步骤之一,其结果的正确性和可靠性将直接影响到机器视觉系统对客观世界的理解。由于目标边缘、图像纹理甚至噪声都可能成为有意义的边缘,因此很难找到一种普适性的边缘检测算法,现有诸多边缘检测的方法各有其特点,同时也都存在着各自的局限性和不足之处,因此图像的边缘检测这个领域还有待于进一步的改进和发展。提出了一种结合梯度和统计均值相对差的蚁群优化方法进行图像边缘检测。 提取梯度值和统计均值的相对差值用于蚂蚁的搜索。 实验结果表明所提算法的优越性能。
2 仿真代码
clc
clear all
close all
% Input:
% gray image with a square size
%
% Output:
% four edge map images, which are obtained by the method using four functions,
% respectively.
%
close all; clear all; clc;
% image loading
filename = 'camera128';
img = double(imread([filename '.bmp']))./255;
[nrow, ncol] = size(img);
%visiblity function initialization, see equation (4)
% generate edge map matrix
% It uses pheromone function to determine edge?
T = func_seperate_two_class(p); %eq. (13)-(21), Calculate the threshold to seperate the edge map into two class
fprintf('Done!\n');
imwrite(uint8(abs((p>=T).*255-255)), gray(256), [filename '_edge_aco_' num2str(nMethod) '.bmp'], 'bmp');
end % end of nMethod
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3 运行结果
4 参考文献
[1] Jian Z , He K , Zheng X , et al. An Ant Colony Optimization Algorithm for Image Edge Detection[C]// Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on. IEEE, 2010.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
