资源列表
shearlet_toolbox(1)
- 这个程序包是矩阵的稀疏表示，适合用于图像处理，简单易懂-This package is a sparse matrix suitable for image processing, simple and easy to understand
huibo
- SAR面目标回波仿真 包含3个目标三维矩阵的mat文件 分别对着三种不同面目标可以进行回波仿真 直接运行即可-The SAR surface targets echo simulation contains 3 target three-dimensional matrix mat file against the three different surface targets echo simulation can be run directly
SR
- 生成显著图方法，一种基于空间频域分析的显著性分析算法（Spectral Residual，SR算法）的matlab实现代码-Matlab code generated saliency map method based on the spatial frequency domain analysis of significant analysis algorithm (Spectral Residual, SR algorithm)
kernel-function
- 核函数的画法 包括全局核函数，局部核函数，分段核函数。-The painting of the kernel function including global nuclear function, local kernel function, segmented kernel function.
harrisdetect
- MATLAB的HARRIS焦点检测以及匹配源程序，主要用于提取兴趣点，图像拼接的重要过程。-The MATLAB HARRIS focus detection and matching source, is mainly used to extract points of interest, image stitching process.
Hu_MV
- matlab实现图像HU不变距。文件中包含测试图片，在主函数上改变图像位置，以选择不同图像。-matlab images HU unchanged from the. The file contains a test picture, change the image location to select a different image in the main function.
qx_recursive_bilateral_filtering
- 杨庆雄recursive bilateral filter code-recursive bilateral filter code
matching
- 基于核线约束的最小二乘匹配，可以提取特征点-least squares matching
HC-visual-detection-by-matlab
- 本资源是清华大学程明明在2011年CVPR上发表的一篇关于视觉显著性的代码，资源包括原文及matlab版本的代码。-It is a source code of visual saliency detection proposed by M.M Cheng in cvpr2011.And it is coded with matlab.
Wavelet_SL0
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
image-registrstionSURF
- 基于OPENCV的图像配准代码，基于SURF算法-image registrstion based on SURF in VS2008
LBP.cpp
- LBP算子的实现源代码，可以根据LBP算子来对2张图片的相似度进行比较，值越小，图像越相似。-LBP operator realization of the source code, you can compare LBP operator to the similarity of the two pictures, the smaller the value, the more similar the image.