minimum-watershed

说明：  水下图像融合，图像融合，matlab语言(Underwater image fusion, image fusion, MATLAB language)

利用matlab提取掌纹图像的感兴趣区域，ROI提取(Extract palmprint images using matlab region of interest, ROI extraction)

数字图像处理 哈里斯脚点检测算法实现 图形界面完全 先高斯滤波 在监测角点(image processing Harris coner dection GUssian filter)

Gabor 小波变换用于面部表情特征提取(Gabor Transform用于表情识别)

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)

小波算法。机械化的提高我们的效率。让我们减少错误率。大家要对她有性心。(Wavelet algorithm. Mechanization to improve our efficiency. Let us reduce the error rate. We have to have sex to her heart.)

说明：  matlab实现的简单的车牌识别，附有相关模板库(matlab chepaishibie,and xiangguanmubanku)

简单的帧间差分法，不过可以和其他方法结合来改进算法(A simple inter-frame difference method, but other methods can be combined to improve the algorithm for)

说明：  光流场经典算法-LK算法，通过两幅图像计算光流场。(Lucas – Kanade Motion Estimation using Pyramids)

基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大(content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of similarity : Mahalanobis distance, the Euclidean distance relevance feedback : machine learning methods, such as SVM. Neural network search and classification : two very similar samples from the small, although the two are similar to the samples may not be much distance)