SIFT_VC.lib

说明：  ：针对显微图像分析、识别需要的全自动控制显微镜，研究开发其相应的图像处理算法。提 出了能量谱自动聚焦评价函数算法、自适应选取基准图的图像拼接算法、改进 Laplacian算子的多层 聚焦图像叠合算法等。该算法模块已成功地应用于所开发的CMIAS 显微医学图像分析系统中，取得 了满意的应用效果。(【ABSTRACT】For the automatic microscope required by micro-image analysis and recognition, the relevant image processing a lgorithms are investigated. Three algorithms are described: energy-spectrum image auto focusing evaluation function, image merging based on the adaptive selection of standard image and sum-modified-Laplacian (SML) operator for multi-focus image fusion. The algorithms have been applied successfully to CMIAS medical micro-optical image analysis system and testified to be feasible and effective. )

本程序实现位图的读写，均衡化，滤波，LBP，LTP特征提取以及基于LBP，LTP和卡方距离，最近邻分类的人脸识别，批量给图片改名等功能(The program can read and write a bitmap, equalization, filtering, LBP, LTP feature extraction and nearest neighbor classifier for face recognition based on LBP, LTP and chi-square distance, rename picture and some other functions.)

设置位置断点跟踪程序，VC++精选编程源码，很好的参考资料。(Set breakpoint location tracking procedures, VC++ programming source code, a good reference.)

用分水岭算法对三维模型进行分割处理。可以实现对三维模型的有意义分割。(Using three-dimensional model of the watershed algorithm to be dealt with separately. Three-dimensional model can achieve meaningful partition.)

文中主要研究使用普通的摄像头进行人眼视线方向的估计。在综合介绍了各种人眼视线检测方法的基础上提出了一种新的图像处理的方法来检测人眼视线方向(The main study used an ordinary camera line of sight of the human eye is estimated. In the consolidated a variety of the human eye sight detection method based on a new image processing method to detect the line of sight of the human eye)

sift 的c++代码。。。。。。。。。。。。(sift c++ code)

论解读DICOM医学图像文件及其显示的VC++实施方法 是一份比较新的讨论dicon的vc实现方法的文章(read DICOM medical images and documents show that the implementation of the VC is a relatively new discussion di vc con method of the article)

96个蛮实用的PNG格式图标 仅供编程或图像处理使用。(96 pretty practical PNG format icon super practical!)

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 ,)

一段很好的图像放大缩小程序，解码后可以运行。(A very good image magnification narrow program, the decoding can run. )