nonmaxsup

利用MATLAB对图像边界内部的部分进行填充，以便后期对图像处理(Using MATLAB within the boundaries of the part of the image to fill, so that the latter part of the image processing)

用matlab对图像进行锐化，加噪，滤波，平滑滤波，中值滤波等处理(Matlab is used to sharpen the image, add noise, filter, smooth filter, median filter and so on)

MCA 图像分解,适用于初次基础图像分解的新手，亲测可用，希望对大家有帮助(MCA image decompositon)

实验一 直线生成算法 实验二 圆弧生成算法 实验三 窗口菜单交互界面 实验四 区域填充算法 实验五 裁剪算法算法 实验六 图形软件开发包/库应用 实验七 曲线的生成算法 实验八 曲面生成算法 实验九 二维图形变换(Experimental line generation algorithm experiment two arcs to generate the algorithm experimental three-window menu interface experiment four regional filling algorithm experimental five-clipping algorithm algorithm experiment six graphics software development package/library application experiment seven curve generation algorithm experiment eight surface generation algorithm experiments ninety-two dimensional graphics transform)

基于T熵的多阈值分割,实现了灰度图像的自动转换,以及对灰度图像的二阈值和三阈值的分割算法(T entropy-based multi-threshold segmentation grayscale image automatically converted, and the threshold grayscale images and three threshold segmentation algorithm)

图像分割在医学图像，智能识别上的应用，虹膜识别(Image segmentation in medical imaging, intelligent recognition on the application of iris recognition)

GUI的指纹特征提取,其中用到的处理函数可以自己编写，也可以直接使用Matlab的库函数(Fingerprint character extract in GUI form, within which the functions can be either designed by users or apply the available functions in Matlab)

超像素分割程序，既有原码，也有可执行程序，验证过，好用。(Super-pixel segmentation procedure, both the original code, there are executable programs, proven, easy to use.)

寻找最低像素值的matlab实现：通过图像矩阵的循环查找，能实现最低像素值的快速寻找(Matlab find the lowest pixel value to achieve: to find the cycle through the image matrix, to achieve the minimum pixel value Quick Find)

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)