c1700-ios

图像拼接源码实现，包括多频率图像融合，sift特征点配准算法(Image Stitching source implementation, including multi-frequency image fusion, sift feature points matching algorithm)

亚像素位移和变形梯度测量采用数字图像/散斑相关(Subpixel displacement and deformation gradient measurement using digital image/speckle correlation(„ DISC… ))

一种基于小波分析的彩色图像融合方法，需要同一场景的红外与夜视各一张，同时选取任意场景合适的彩色照片作为参考图像(A color image fusion method based on wavelet analysis, infrared and night vision needs each one of the same scene, the scene at the same time any suitable color photo as a reference image)

说明：  matlab工具箱，用于构建和重建可分离的正交可操纵金字塔。(a matlab toolbox for building and reconstructing separable quadrature steerable pyramids.)

两个l1准则下的噪声干扰信号压缩感知重构举例，两个例子的稀疏矩阵均为DCT矩阵，而观测矩阵分别采用单位阵和随机矩阵，有详细的步骤和使用方法，适用于初步的学习压缩感知方法。(This programme supply two examples by Compressed sensing with l1 norm. The sparse matrix of two examples are all DCT matrix and the obsever matrix are unit matrix and random matrix respectively. We give the detailed usage and steps for the programme which is useful to the beginner.)

用于摄像机标定的Harris角点检测代码码。能达到亚像素精度。压缩包内附说明文档。 (Harris corner detection for camera calibration code code. To achieve sub-pixel accuracy. The compressed package included documentation.)

robustly fits a homography to a set of putatively matched image points(robustly fits a homography to a set of putat ively matched image points)

基于小波变换的图像融合源程序，通过调试，能直接使用。(Image fusion based on wavelet transform source code through the debugger can be used directly.)

改进的剪切波变换，用于图像处理，分解层数多，运行速度快(Improved shear wave transform for image processing, decomposition of many layers, fast running speed)

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)