color

read envi headers images

compressed sening image compresseion

基于粒子滤波的视频目标跟踪算法matlab源程序，内涵视频文件及算法源程序。(Video object tracking algorithm based on particle filter matlab source program, content video files and algorithm source program.)

用MATLAB实现亚像素角点检测，简单实用(Using MATLAB to achieve sub-pixel corner detection, simple and practical)

在这个程序中，我们读取两张图像，进行特征匹配。然后根据匹配得到的特征，计算相机运动以及特征点的位置。这是一个典型的Bundle Adjustment，我们用g2o进行优化。

图形处理，Visual C++图形编程技巧与实例源代码3(Graphics Visual C++ graphics programming techniques and source code examples 3)

说明：  matlab编写高斯光束高斯光束模拟的焦点附近光场分布(Matlab programming of Gaussian beam the distribution of light field near the focus of Gaussian beam simulation)

说明：  Donoho提出的小波阀值去噪的基本思想是将信号通过小波变换（采用Mallat算法）后，信号产生的小波系数含有信号的重要信息，将信号经小波分解后小波系数较大，噪声的小波系数较小，并且噪声的小波系数要小于信号的小波系数，通过选取一个合适的阀值，大于阀值的小波系数被认为是有信号产生的，应予以保留，小于阀值的则认为是噪声产生的，置为零从而达到去噪的目的。尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点。(The basic idea of wavelet threshold de-noising proposed by Donoho is that the wavelet coefficients generated by the signal after passing through the wavelet transform (Mallat algorithm) contain the important information of the signal. After the wavelet decomposition, the wavelet coefficients of the signal are larger, the wavelet coefficients of the noise are smaller, and the wavelet coefficients of the noise are smaller than the wavelet coefficients of the signal. By selecting a suitable threshold, the wavelet coefficients of the noise are larger than the valve The wavelet coefficients of values are considered to be generated by signals, which should be preserved. The ones less than the threshold are considered to be generated by noise, which is set to zero to achieve the purpose of denoising.)

用于通过三维激光点云数据生成二维的地理参考特征图像，此处以高程作为特征(Used by 3 d laser point cloud data to generate two-dimensional geographical characteristics of reference image, the elevation as features)

说明：  This is BM3D algorithm implemented according to the paper: K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August 2007.(This is BM3D algorithm implemented according to the paper: Image denoising by sparse 3D transform-domain collaborative filtering)