不同颜色空间中的鲁棒边缘检测附matlab代码

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智能优化算法       神经网络预测       雷达通信       无线传感器        电力系统

信号处理              图像处理               路径规划       元胞自动机        无人机 

⛄ 内容介绍

Edge detection is very important technique to reveal significant areas in the digital image, which

could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of preand post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are sopromising and satisfactory in compare with other methods available in validation section of the paper..

⛄ 部分代码

clc;

clear;

img=imread('teapot.jpg');

org=img;

img=imbinarize(img);

se1 = strel('disk',1);

se2 = strel('disk',3);

erodedBW = imerode(img,se1);

closeBW = imclose(erodedBW,se2);

subplot(1,4,1)

subimage(org); title('Original');

subplot(1,4,2)

subimage(img);title('BW');

subplot(1,4,3)

subimage(erodedBW); title('SE= Disc, Erode');

subplot(1,4,4)

subimage(closeBW); title('SE=Disc, Closing');

⛄ 运行结果

⛄ 参考文献

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