Image segmentation refers to the process of dividing an image into multiple sub-regions or pixel sets, where each sub-region or pixel set has certain statistical characteristics or semantic information. Image segmentation is a basic task in image processing, and its applications cover many fields such as medical imaging, computer vision, and robotics. Commonly used image segmentation algorithms include:
1. Threshold-based segmentation algorithm: divide the pixels in the image into several regions according to their gray value, and usually use single threshold, multi-threshold and adaptive threshold for segmentation. The algorithm is simple and easy to understand, and is suitable for images with high contrast, but it has a greater impact on factors such as illumination and noise.
2. Edge-based segmentation algorithm: Segmentation is performed by detecting edges or contours in the image. Commonly used algorithms include Canny algorithm, Sobel algorithm, etc. This algorithm works better for images with obvious edges, but poorer for images with noise and complex backgrounds.
3. Region-based segmentation algorithm: divide the pixels in the image into several regions, and segment them by the similarity between regions. Commonly used algorithms include K-means algorithm, watershed algorithm, etc. This algorithm works better for images with complex backgrounds and more noise, but it is difficult to evaluate and optimize the segmentation results.
4. Energy-based segmentation algorithm: Image segmentation is performed by defining an energy function. Commonly used algorithms include GrabCut algorithm, GraphCut algorithm, etc. This algorithm has a good effect on image segmentation, but has high computational complexity and requires a long running time.
The threshold-based segmentation algorithm is a simple but effective image segmentation method. The algorithm divides the image into foreground and background parts according to the gray value of the pixels.
The algorithm flow is as follows:
1. Choose a threshold T.
2. Traverse each pixel in the image and compare the gray value of the pixel with the threshold T.
3. If the gray value of the pixel is less than the threshold T, mark the pixel as background; otherwise, mark the pixel as foreground.
4. The final image is the segmented image.
The advantage of this algorithm is that it is simple and easy to use, and the calculation speed is fast. But the disadvantage is that you need to manually select the appropriate threshold, and different thresholds are required for different images, so this algorithm is suitable for segmenting relatively simple images.
The following is a Matlab code implementation of a threshold-based segmentation algorithm:
% read image
img = imread('image.jpg');
% Convert the image to a grayscale image
gray_img = rgb2gray(img);
% select threshold
T = 128;
% split image
binary_img = gray_img > T;
% Display segmentation results
subplot(1, 2, 1), imshow(gray_img);
title('original image')
subplot(1, 2, 2), imshow(binary_img);
title('segmented image')
In the above code, we first read in an image and converted it to a grayscale image. Then select a threshold T to segment the grayscale image to obtain a binary image. Finally, use the subplot function to display the original image and the segmented image in the same window.
The region-based segmentation algorithm is a method of segmentation based on the characteristics of the local area of the image. It usually divides the image into different regions according to the characteristics such as grayscale and color between adjacent pixels.
The algorithm flow is as follows:
1. Divide the image into several small regions.
2. Calculate the characteristics of each region, such as grayscale, color, etc.
3. According to the similarity between adjacent regions, the regions are merged to obtain a larger region.
4. Repeat step 3 until all regions are merged into one region.
Region-based segmentation algorithms are usually implemented using clustering algorithms, such as k-means algorithm, mean shift algorithm, etc. The advantage of this algorithm is that it can be segmented according to the characteristics of the local area of the image, and it is suitable for processing complex images.
The following is a Matlab code implementation of a region-based segmentation algorithm, which uses the k-means algorithm:
% read in the image
img = imread('image.jpg');
% Convert image to Lab color space
lab_img = rgb2lab(img);
% Divide the image into several small areas
num_regions = 100;
[height, width, ~] = size(lab_img);
pixel_labels = zeros(height, width);
num_pixels = height * width;
rand_indices = randperm(num_pixels);
for i = 1:num_regions
pixel_labels(rand_indices(i)) = i;
end
% Calculate the features of each region
features = zeros(num_pixels, 3);
for i = 1:num_pixels
[row, col] = ind2sub([height, width], i);
features(i, :) = lab_img(row, col, :);
end
% Use the k-means algorithm to merge similar areas
num_clusters = 10;
cluster_labels = kmeans(features, num_clusters, 'Distance', 'sqEuclidean', 'Replicates', 3);
% Map the merged labels to pixels
segmented_images = cell(1, num_clusters);
rgb_label = repmat(pixel_labels, [1, 1, 3]);
for i = 1:num_clusters
color = img;
color(rgb_label ~= cluster_labels(i)) = 0;
segmented_images{i} = color;
end
% Display segmentation results
figure();
subplot(1, num_clusters+1, 1);
imshow(img);
title('original image');
for i = 1:num_clusters
subplot(1, num_clusters+1, i+1) ;
imshow(segmented_images{i});
title(sprintf('Segment%d', i));
end
```
In the above code, we first read in an image and converted it to Lab color space. Then the image is divided into several small areas, and similar areas are merged using the k-means algorithm to obtain a segmented image. Finally, use the subplot function to display the original image and the segmented image in the same window.
The edge-based segmentation algorithm is a method of segmentation based on image edge information, which usually uses edge detection algorithm to extract the edge information in the image, and then divides the image into different regions according to the edge information.
The algorithm flow is as follows:
1. Perform edge detection on the image to obtain the edge image.
2. Segment the image into different regions according to the edge image.
3. Perform post-processing on each region, such as filling, smoothing, etc., to obtain more accurate segmentation results.
Commonly used edge detection algorithms include Sobel operator, Canny operator and so on. The advantage of the edge-based segmentation algorithm is that it can be segmented according to the edge information of the image, and it is suitable for processing images with obvious edges.
The following is a Matlab code implementation of an edge-based segmentation algorithm, which uses the Canny operator for edge detection:
% read image
img = imread('image.jpg');
% Convert the image to a grayscale image
gray_img = rgb2gray(img);
% Use Canny operator to detect edge
edge_img = edge(gray_img, 'Canny');
% Perform morphological operations on edge images, filling edge breaks
se = strel('disk', 5);
dilated_edge_img = imdilate(edge_img, se);
% Perform connected region analysis on the binary image, and divide the image into different regions
cc = bwconncomp(dilated_edge_img);
num_regions = cc.NumObjects;
% Display different areas as different colors
rgb_label = label2rgb(labelmatrix(cc), 'jet', 'k', 'shuffle');
% Display segmentation results
figure();
subplot(1, 2, 1);
imshow(img);
title('original image');
subplot(1, 2, 2);
imshow(rgb_label);
title(sprintf('segmentation into %d regions', num_regions));
In the above code, we first read in an image and converted it to a grayscale image. Then use the Canny operator to detect the edge of the image, and perform morphological operations on the edge image to fill the edge break. Then use the bwconncomp function to analyze the connected regions of the binary image to obtain a label matrix containing multiple regions. Finally, use the label2rgb function to convert the label matrix into a color image, and display different regions as different colors.
The energy-based segmentation algorithm is a method of segmentation based on the principle of image energy minimization. It usually obtains the image segmentation result by minimizing the energy function according to the similarity and connectivity between image pixels.
The algorithm flow is as follows:
1. Define the energy function, which usually includes two parts: data item and smoothing item.
2. Initialize the segmentation results, usually using random initialization or initialization methods based on other algorithms.
3. Iteratively optimize the energy function to obtain the optimal segmentation result.
Commonly used energy functions include Potts model, Markov random field model, etc. The advantage of the energy-based segmentation algorithm is that it can be segmented according to features such as similarity and connectivity between image pixels, and is suitable for processing complex images. But the disadvantage is that the calculation complexity is high, and it needs to consume a lot of time and computing resources.
The following is a Matlab code implementation of an energy-based segmentation algorithm, which uses the Potts model as the energy function:
% read in the image
img = imread('image.jpg');
% Convert the image to a grayscale image
gray_img = rgb2gray(img);
% Define the parameter
lambda of the Potts model = 1; % The weight of the smoothing item
beta = 1; % The weight of the data item
num_labels = 2; % The number of labels for the segmentation result
% Initialize the segmentation result
label_img = randi(num_labels, size(gray_img));
% Iteratively optimize the energy function
for iter = 1:100
% Calculate the energy of data items
data_energy = 0;
for i = 1:numel(gray_img)
[row, col] = ind2sub(size(gray_img), i);
neighbors = get_neighbors( row, col, size(gray_img));
for j = 1:numel(neighbors)
if label_img(row, col) ~= label_img(neighbors(j))
data_energy = data_energy + beta;
end
end
end
% Calculate the energy of the smoothing term
smooth_energy = 0;
for label = 1:num_labels
[x, y] = find(label_img == label);
for i = 1:numel(x)
neighbors = get_neighbors(x(i), y(i), size(gray_img ));
for j = 1:numel(neighbors)
if label_img(x(i), y(i)) ~= label_img(neighbors(j))
smooth_energy = smooth_energy + lambda;
end
end
end
end
% Calculate the total energy
total_energy = data_energy + smooth_energy;
fprintf('Iteration %d: Total energy = %f\n', iter, total_energy);
% update segmentation results
label_img = graphcut(gray_img, label_img, 'smoothness', lambda, 'weight', beta);
end
% Display segmentation results
figure();
subplot(1, 2, 1);
imshow(img);
title('original image');
subplot(1, 2, 2);
imshow(label2rgb(label_img));
title(sprintf ('Split into %d regions', num_labels));
In the above code, we first read in an image and converted it to a grayscale image. Then the parameters of the Potts model are defined, and the segmentation results are initialized by random initialization. Then use the graphcut function to iteratively optimize the energy function to obtain the optimal segmentation result. Finally, use the label2rgb function to convert the label matrix into a color image, and display different regions as different colors.
It should be noted that in the above code, we define a get_neighbors function to obtain the position of the surrounding pixels of a certain pixel. The specific implementation is as follows:
function [neighbors] = get_neighbors(row, col, img_size)
% 获取某个像素的周围像素的位置
neighbors = [];
if row > 1
neighbors = [neighbors sub2ind(img_size, row-1, col)];
end
if col > 1
neighbors = [neighbors sub2ind(img_size, row, col-1)];
end
if row < img_size(1)
neighbors = [neighbors sub2ind(img_size, row+1, col)];
end
if col < img_size(2)
neighbors = [neighbors sub2ind(img_size, row, col+1)];
end
end
This function inputs the row and column coordinates of a pixel and the size of the image, and outputs the position of the pixels around the pixel.
The graphcut function in the code implementation is a function in the Matlab toolbox for graph cut segmentation. The calling format of this function is as follows:
[label, energy] = graphcut(I, mask, varargin)
```
Among them, I is the input image, mask is a binary mask, which is used to specify the position of the foreground and background pixels, and other parameters are used to specify parameters such as the weight of the smoothing item and the data item.
In the above code, we use the graphcut function to split and specify the weights of the smooth and data items. Here we set the weight lambda of the smoothing item and the weight beta of the data item to 1 respectively, and the number of labels num_labels of the segmentation result to 2. In the iterative process, we calculated the energy of the data term and the smooth term, and added them to get the total energy. Then use the graphcut function to update the segmentation results and output the total energy for the current iteration.
It should be noted that the calculation complexity of the energy-based segmentation algorithm is high, and the number of iterations generally needs to be set to a large value, such as 100 in the above code. At the same time, parameters need to be adjusted according to specific images and application scenarios to obtain optimal segmentation results.
Usage of the label2rgb function, which is a function in the Matlab toolbox for converting a label matrix into a color image. The calling format of this function is as follows:
rgb = label2rgb(L, cmap, 'name', value, ...)
```
Among them, L is the label matrix, cmap is the color mapping table, which is used to specify the color of different labels, and other parameters are used to specify the range of the color mapping table, color space and other parameters.
In the above code, we use the label2rgb function to convert the label matrix label_img into a color image. Since the segmentation result has only two regions, we did not specify the color map cmap, but used the default color map. Finally, we display the original image and the segmentation result on the same image for comparison and observation.
It should be noted that when using the label2rgb function, an appropriate color mapping table needs to be selected according to specific application scenarios and requirements.