NDVI批量计算Python代码

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

非常经典的彩色转移算法的原文及源代码，是学习彩色转移必须看的。(The text of the classic color transfer algorithm and the source code, is the study of color transfer must see it. )

用于图像拷贝检测的图像匹配，提取图像的SIFT特征点，构成图像的特征。(For image copy detection of image matching, image SIFT feature point extraction, and then sift feature point the image characteristics)

contourlet对LENA512灰度图的LP,DEF分解与系数重构代码,经过调试.(Contourlet LENA512 grayscale of the LP, DEF coefficient decomposition and reconstruction of the code after debugging.)

干涉SAR图像配准程序，matlab编的，很好用！(Interferometric SAR image registration procedures, matlab, and easy to use!)

一篇基于Expected Patch Log Likelihood的图像去噪文章及其实现(One based on Expected Patch Log Likelihood Denoising article and its realization)

图像处理，可以用来进行图像内插。效果还不错的！(image interpolation)

用于图象处理的小波变换并行算法,本文在二进小波变换及Mallat快速算法原理的基础上，分析了小波变换的 可并行成分，研究了小波变换的并行算法.算法分析表明，并行小波变换可以有效 地提高运行速度，其并行处理效率也接近理想状态.(image processing for the wavelet transform parallel algorithm, In this paper, two Progressive Mallat wavelet transform and fast algorithm on the basis of principle, Analysis of the wavelet transform the parallel components of wavelet transform the parallel algorithm. algorithm analysis showed that Parallel wavelet transform effectively increase the operating speed and its parallel processing efficiency is close to the ideal state.)

利用霍夫变换进行直线检测的算法实现 设置不同的阈值来控制直线的输出(detect lines in image)

图像检索。通过计算所要检索的图像与图像库中的距离dis，dis最小者与检索图像同类。(image retrieval. Calculated to be indexed images and pictures of the dis the distance, the smallest dis with similar image retrieval.)