MATLAB实现均漂移的函数

以下是MATLAB实现均漂移的函数：

function [outputImage] = meanShift(inputImage, spatialRadius, colorRadius, numIterations)
% Mean Shift Algorithm
% inputImage - input image
% spatialRadius - spatial radius of kernel
% colorRadius - color radius of kernel
% numIterations - number of iterations
% outputImage - output image

% Convert input image to double precision
inputImage = im2double(inputImage);

% Create output image of same size and type as input image
outputImage = zeros(size(inputImage), 'like', inputImage);

% Convert spatial and color radius to squared distance
spatialRadiusSq = spatialRadius^2;
colorRadiusSq = colorRadius^2;

% Iterate over all pixels in the input image
for i = 1:size(inputImage, 1)
    for j = 1:size(inputImage, 2)
        
        % Initialize mean shift vector and total weight
        meanShiftVec = [0 0];
        totalWeight = 0;
        
        % Iterate over all pixels in the spatial and color neighborhood
        for k = i-spatialRadius:i+spatialRadius
            for l = j-spatialRadius:j+spatialRadius
                
                % Compute distance between current pixel and center pixel
                spatialDistSq = (k-i)^2 + (l-j)^2;
                if (spatialDistSq > spatialRadiusSq)
                    continue;
                end
                
                % Compute distance between current pixel and center pixel in color space
                colorDistSq = sum((inputImage(i,j,:) - inputImage(k,l,:)).^2);
                if (colorDistSq > colorRadiusSq)
                    continue;
                end
                
                % Compute weight for current pixel
                weight = exp(-0.5*spatialDistSq/spatialRadiusSq - 0.5*colorDistSq/colorRadiusSq);
                
                % Update mean shift vector and total weight
                meanShiftVec = meanShiftVec + weight*[k l];
                totalWeight = totalWeight + weight;
            end
        end
        
        % Compute mean shift vector
        meanShiftVec = meanShiftVec/totalWeight;
        
        % Iterate over specified number of iterations
        for iter = 1:numIterations
            
            % Compute distance between current pixel and mean shift vector
            distSq = sum((meanShiftVec - [i j]).^2);
            
            % If distance is zero, break out of loop
            if (distSq == 0)
                break;
            end
            
            % Initialize new mean shift vector and total weight
            newMeanShiftVec = [0 0];
            totalWeight = 0;
            
            % Iterate over all pixels in the spatial and color neighborhood
            for k = i-spatialRadius:i+spatialRadius
                for l = j-spatialRadius:j+spatialRadius
                    
                    % Compute distance between current pixel and center pixel
                    spatialDistSq = (k-i)^2 + (l-j)^2;
                    if (spatialDistSq > spatialRadiusSq)
                        continue;
                    end
                    
                    % Compute distance between current pixel and center pixel in color space
                    colorDistSq = sum((inputImage(i,j,:) - inputImage(k,l,:)).^2);
                    if (colorDistSq > colorRadiusSq)
                        continue;
                    end
                    
                    % Compute weight for current pixel
                    weight = exp(-0.5*spatialDistSq/spatialRadiusSq - 0.5*colorDistSq/colorRadiusSq);
                    
                    % Update new mean shift vector and total weight
                    newMeanShiftVec = newMeanShiftVec + weight*[k l];
                    totalWeight = totalWeight + weight;
                end
            end
            
            % Compute new mean shift vector
            newMeanShiftVec = newMeanShiftVec/totalWeight;
            
            % Compute distance between new and old mean shift vectors
            distSq = sum((newMeanShiftVec - meanShiftVec).^2);
            
            % If distance is less than threshold, break out of loop
            if (distSq < 0.1)
                break;
            end
            
            % Update mean shift vector
            meanShiftVec = newMeanShiftVec;
        end
        
        % Assign output value to mean shift vector
        outputImage(i,j,:) = inputImage(round(meanShiftVec(1)), round(meanShiftVec(2)),:);
    end
end

end

该函数接受四个参数：输入图像、空间半径、颜色半径和迭代次数。它使用均值漂移算法对输入图像进行平滑处理，并返回平滑后的图像。

该函数首先将输入图像转换为双精度浮点数，并创建一个与输入图像大小和类型相同的输出图像。接下来，函数将空间和颜色半径转换为平方距离，并迭代所有像素。对于每个像素，函数计算其空间和颜色邻域中所有像素的权重，并使用这些权重计算平均漂移向量。然后，函数迭代指定的次数，并计算新的平均漂移向量。最后，函数将输出值赋值给平均漂移向量。

请注意，该函数使用两个嵌套的循环来迭代所有像素和邻域像素。这可以导致算法的运行时间较长，特别是对于大型图像。为了加速算法，可以使用更高效的数据结构，例如kd树或四叉树，来查找邻域像素。