matlab中Canny边缘检测算法
时间: 2023-11-20 12:44:35 浏览: 35
的具体原理是什么?
Canny边缘检测算法是一种经典的图像边缘检测算法,其主要原理是在求出图像梯度的基础上,通过非最大值抑制和双阈值检测等步骤来提取图像中的边缘特征。具体而言,Canny边缘检测算法通过先对图像进行高斯滤波,提高图像的信噪比,然后计算图像中每个像素点的梯度强度和方向,进而在梯度方向上进行非最大值抑制,保留像素点梯度方向上的最大值,同时对梯度较小的像素点进行抑制。最后,通过设置双阈值来确定图像中的强边缘和弱边缘,并对弱边缘进行连接,以获得完整的边缘信息。
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MATLAB实现canny边缘检测算法的代码
以下是MATLAB实现canny边缘检测算法的代码,仅供参考:
%% Canny Edge Detection Algorithm
% Define input image
I = imread('test_image.jpg');
% Convert image to grayscale
gray_I = rgb2gray(I);
% Define parameters for edge detection
sigma = 1.5; % Gaussian filter sigma value
Th = 0.1; % Low threshold value
Th_H = 0.2; % High threshold value
% Apply Gaussian filter to the input image
g = fspecial('gaussian',[3 3],sigma);
smooth_I = imfilter(gray_I,g,'same');
% Compute gradient magnitude and direction
[Ix, Iy] = imgradientxy(smooth_I,'sobel');
% Compute gradient direction
theta = atan2(Iy,Ix);
% Perform non-maximum suppression
[suppressed_I, suppressed_theta] = nonmaxsupp(Ix,Iy,theta);
% Thresholding
edge_map = double(suppressed_I > Th_H);
% Hysteresis thresholding
edge_map = hystthresh(edge_map,Th,Th_H);
% Display the final edge map
imshow(edge_map);
function [suppressed_I, suppressed_theta] = nonmaxsupp(Ix, Iy, theta)
% Obtain the input image size
[rows,cols] = size(Ix);
% Initialize the output image
suppressed_I = zeros(rows,cols);
% Compute non-maximum suppression
for i = 2:rows-1
for j = 2:cols-1
% Check the gradient direction and interpolate the gradient magnitude
% along the direction perpendicular to the direction
if (theta(i,j) >= -pi/8 && theta(i,j) < pi/8) || (theta(i,j) >= 7*pi/8 && theta(i,j) <= pi) || (theta(i,j) >= -pi && theta(i,j) < -7*pi/8)
if Ix(i,j) > Ix(i,j-1) && Ix(i,j) > Ix(i,j+1)
suppressed_I(i,j) = Ix(i,j);
suppressed_theta(i,j) = theta(i,j);
end
elseif (theta(i,j) >= pi/8 && theta(i,j) < 3*pi/8) || (theta(i,j) >= -7*pi/8 && theta(i,j) < -5*pi/8)
if Ix(i,j) > Ix(i-1,j+1) && Ix(i,j) > Ix(i+1,j-1)
suppressed_I(i,j) = Ix(i,j);
suppressed_theta(i,j) = theta(i,j);
end
elseif (theta(i,j) >= 3*pi/8 && theta(i,j) < 5*pi/8) || (theta(i,j) >= -5*pi/8 && theta(i,j) < -3*pi/8)
if Ix(i,j) > Iy(i-1,j) && Ix(i,j) > Iy(i+1,j)
suppressed_I(i,j) = Ix(i,j);
suppressed_theta(i,j) = theta(i,j);
end
elseif (theta(i,j) >= 5*pi/8 && theta(i,j) < 7*pi/8) || (theta(i,j) >= -3*pi/8 && theta(i,j) < -pi/8)
if Ix(i,j) > Ix(i-1,j-1) && Ix(i,j) > Ix(i+1,j+1)
suppressed_I(i,j) = Ix(i,j);
suppressed_theta(i,j) = theta(i,j);
end
end
end
end
end
function edge_map = hystthresh(edge_map,Th, Th_H)
% Obtain the input image size
[rows,cols] = size(edge_map);
% Initialize the output image
edge_map = double(edge_map);
% Perform hysteresis thresholding
for i = 2:rows-1
for j = 2:cols-1
if edge_map(i,j) > Th_H % Strong edge pixel
edge_map(i,j) = 1;
elseif edge_map(i,j) < Th % Weak edge pixel
edge_map(i,j) = 0;
elseif edge_map(i-1,j-1) > Th_H || edge_map(i-1,j) > Th_H || edge_map(i-1,j+1) > Th_H || edge_map(i,j-1) > Th_H || edge_map(i,j+1) > Th_H || edge_map(i+1,j-1) > Th_H || edge_map(i+1,j) > Th_H || edge_map(i+1,j+1) > Th_H
edge_map(i,j) = 1;
end
end
end
end
canny边缘检测算法matlab
canny边缘检测算法是一种常用的图像边缘检测方法,它能够有效地抑制噪声并精确地确定边缘的位置。该算法的步骤如下:
1. 首先,将图像转换为灰度图像(如果原图是彩色图像)。
2. 对灰度图像进行高斯滤波,以平滑图像并消除噪声。
3. 计算图像中每个像素的梯度幅值和方向。
4. 对梯度幅值进行非极大值抑制,以细化边缘。
5. 应用双阈值来确定真正的边缘。具体来说,设置一个低阈值和一个高阈值,高于高阈值的像素被认为是强边缘,低于低阈值的像素被认为是非边缘,介于两者之间的像素被视为弱边缘,可以根据其是否与强边缘相连来决定是否将其视为真正的边缘。
6. 最后,通过连接强边缘和与之相连的弱边缘来得到最终的边缘图像。
以下是使用MATLAB实现canny边缘检测算法的代码:
```matlab
Img = imread('cameraman.tif'); % 读取图像
M = size(Img); % 计算图像大小
if numel(M) > 2 %判断图像是否是彩色图像
gray = rgb2gray(Img); % 图像灰度变换
else
gray = Img;
end
Cimg = edge(gray,'canny');
figure(1);
subplot(121);
imshow(gray,[]);
title('原图');
subplot(122);
imshow(Cimg,[]);
title('Canny边缘检测');
```