subpixelEdgeDetection

用于图像拼接的素材，可用于全景图拼接。主要是JPG形式，可通过SIFT算法进行处理(Material for image stitching, can be used for panorama stitching. Mainly JPG format, can be handled by the SIFT)

图像分割--用matlab实现图像的自动多阈值分割 (Image segmentation- Image by matlab automatic multi-threshold segmentation)

脑机接口中运动想象，经典算法共空间模式CSP(common spacial pattern)

求质心到图像轮廓边缘的距离，并将其用figure表示出来(The distance from the center of mass to the edge of the image contour)

本人用matlab自己做的图像分裂合并程序，主要对印章图像进行处理．(own image split the merger process, mainly to seal image processing.)

主要是多光谱多波段两幅图像的变化检测求得差异影像 大家可以参考参考 运行主程序(mian is used to difference of two images)

matlab程序 关于用变分法去除图像噪声 非常具有参考 价值 希望对学习图像去噪的同学 有帮助(matlab program on the use of variational method to remove image noise very reference value I hope to learn denoising help students)

用MATLAB实现的边缘提取函数，实现了sobel算子、canny算子，Robert算子、prewitt算子、marr算子(Using MATLAB to achieve the edge detection function, the realization of the sobel operator, canny operator, Robert operator, prewitt operator, marr Operator)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

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