MeanShift2

说明：  基于fft的数字图像处理MATLAB实现(Fft based on MATLAB for digital image processing to achieve)

采用最小交叉熵，最大熵，OTSU阈值分割(Using minimum cross entropy, maximum entropy, OTSU thresholding)

基于颜色衰减先验模型的单幅图像快速去雾算法。(A Fast Single Image Haze Removal Algorithm using Color Attenuation Prior)

geotiff_segment.影像分割的小程序，简单易用，非常方便，开发环境IDL.(geotiff_segment.Small image segmentation procedures, simple to use, very, IDL development environment.)

基于Hough变换的虹膜自动分割的matlab代码(Automatically based on Hough transform in iris segmentation matlab code)

基于置信传播的立体匹配算法，匹配精度较高(Stereo matching algorithm based on belief propagation, high matching accuracy)

基于MRF的SAR图像分割，在空域下进行建模，对初学者很有帮助。(SAR image segmentation based on MRF modeling in airspace, useful for beginners.)

这是冈萨雷斯版数字图像处理中的几个函数的实现，包括gmean,以及书中的例题5.7和5.10。(This is the realization Gonzalez version Several digital image processing functions, including gmean, and examples in the book of 5.7 and 5.10.)

针对可见光与红外图像的特点和难点，提出了可见光与红外图像配准与融合中的关键技术，即: 使用新型的基于一维最大类间方差和最大连通性测量的图像分割方法对源图像进行分割来更好地实行图像粗 配准 使用新型的特征点提取方法，特征点的匹配及误匹配的消除来更好地实行图像精配准 采用新型的基 于区域的树状小波活性测度计算来实现树状小波图像融合 利用自生成神经网络来实现模栩图像融合. (For visible light and infrared images of the characteristics and difficulties, a visual and infrared image registration and integration of key technologies, namely: the use of new one-dimensional based on the largest variance between-class connectivity and the largest measuring method of image segmentation source image segmentation to better implementation of coarse image registration the use of a new type of feature point extraction method, feature points of the match and the elimination of false matches to better the implementation of image fine registration the use of new tree-based wavelet calculated to measure the achievement of the activity tree wavelet image fusion the use of self-generating neural network to achieve image fusion Xu mode.)

labview图像预处理，子程序,包括转化为灰度图，二值化，腐蚀，膨胀，参数可调(labview IMAQ image pretreatment,sub vi，consist of conversion to grayscale, binary, erosion, dilation, adjustable parameters)