gdcm-2.2.0

几个大牛对集成了国内外特征提取最前沿的算法，特征提取，DOG,HARRIS,HARRIS-LAPLACE,HARRIS-AFFINE,MSER.描述符，SIFT,AFFINE-SIFT,GLOH.经测试，速度快，描述符准确。(Integration of feature extraction algorithms from several expert of domestic and international, feature extraction： DOG, HARRIS, HARRIS-LAPLACE, HARRIS-AFFINE, MSER； Descriptors： SIFT, AFFINE-SIFT, GLOH. The test reveal the speed and accurate descriptor.)

数字图像处理中常用的标准图像，包括大家所共知的lena等图片的高分辨率版本，推荐大家下载(Commonly used in digital image processing standard images, including all the known to the lena and other high-resolution version of the picture, we recommend downloading)

光流法、帧间差分法、高斯背景模型差分法，在opencv环境下实现运动目标检测(moving objective detection matlab)

用openCV实现，用Camshift算法实现彩色目标跟踪(openCV,color object tracking usingCamshift)

图像分割方法中有关感兴趣区域处理的程序代码(Image segmentation method for region of interest processing code)

张正友标定法,标定图像，将图像进行校正，是当今用的最多的方式方法(This method is so good, I have transferred through this. And it is very useful for the IT workers. This is my first code. Thank you every one.)

载入一副图像并进行平滑处理与高斯或其他核函数进行卷积有效的减少图像信息内容(Loading an image and smoothed with a Gaussian or other nuclear convolution function effectively reduce the information content of the image)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)

说明：  模式识别与图像处理，图像的边缘处理，增强(Pattern recognition and image processing, image edge processing, to enhance)