harris-match

矩形信号的frft（分数傅里叶变换wigner_ville_1.m 单分量信号去噪 wigner_ville_2.m 双分量平行信号去噪 wigner_ville_3.m 3分量信号频率检测）分数傅里叶变换是经典傅里叶变换的另一种改进方式。它的主要特点是提供研究对象从时间域到频率域全过程的综合描述，随着阶数从0连续增长到1，分数傅里叶变换展示出研究对象从纯时间域逐步变化到纯频率域的所有变化特征。因此，分数傅里叶变换提供了远比傅里叶变换多得多的可供选择的数据处理和分析方法。(The rectangular signal FRFT (Fractional Fourier Transform) Score Fourier transform is another modification of the classic Fourier transform. Its main feature is a comprehensive description of the study from the time domain to the frequency domain, with the order of continuous growth from 0 to 1, the fractional Fourier transform demonstrate the object of study from a purely time-domain gradually change to a pure frequency domain variation. Thus, the fractional Fourier transform provides data processing and analysis methods than Fourier transform much more to choose from.)

从xsl文件中读入数据并计算其wigner_ville分布，内含详细说明。(Xsl file reads from the data and calculates its wigner_ville distribution, containing detailed instructions.)

KMS跟踪算法源代码，目前最快的但目标跟踪算法（没有之一）。工程可以直接运行(KMS tracking algorithm source code, but the fastest target tracking algorithm. Project can be run directly)

里面包括了MATLAB的好多程序,主要有灰度切分，直接灰度变换，直方图规定化，直方图均衡化等(Which includes many of the MATLAB program, there are gray-scale segmentation, direct gray-scale transformation, Histogram of the provisions of, such as histogram equalization)

说明：  模式识别与图像处理，图像的边缘处理，增强(Pattern recognition and image processing, image edge processing, to enhance)

说明：  图片自适应二值化处理，二值化处理使用简单方便。(Adaptive binarization image processing, binary processing easy to use.)

修改了冈萨雷斯《数字图像处理》中的区域生长法。本函数中含有三种选择种子点的方法：分别依据灰度值，种子点位置，交互式选择单个种子点。(Modified the region growing method described in "Digital Image Processing" . This function contains three options for the method of seed points: based on gray value, the location of the seed points, interactive selection of a single seed point. )

Image, Edge Detection, Code

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)

基于深度图的图像修复，有完整的代码和实验图像。(Image restoration based on the depth chart, there is a complete code and experimental images.)