MatchLSM

原创基于c++语言编程实现了MC算法在三维图像处理中的应用，以及有许多二维图像到三维图像表面的重建过程算法。通过VC2008，编译已通过。(I have realized MC algorithm about the application of processing 3-D image based on C++ language also realised the technology on image surface reconstruction from 2-D to 3-D image.They are all compiled successfully using VS2008)

图像匹配，可以实现目标图像的匹配，匹配出最相似的训练图片。(Image matching, the matching can achieve the target image, matching the most similar training images.)

说明：  双目测距bm算法，已经实现,基于vs2015+opencv3.2,测距精度很高，一米内精度一厘米误差(Binocular ranging bm algorithm, which has been implemented, is based on vs2015 + opencv3.2, which has high accuracy and one centimeter error in one meter)

多边形填充 多边形裁剪 自定义多边形区域 自定义裁剪区域(Polygon fill polygon clipping custom polygon area custom crop area)

高斯混合模型的源代码。从文件中读取数据，用三个高斯混合模型进行处理(Gaussian mixture model of the source code. Read from the file data, using three Gaussian mixture model for processing)

针对万工显螺旋显微镜视场图像特点，编写了Matlab 语言的图像处理算法用于万工显自动读数( The program which is developed by MATLAB language can be used for auto-reading of universal tools microscope measurement result)

fpga实现图象滤波，实时的实现现对输入图象的形态学滤波可直接使用。 (fpga image filtering, real-time implementations currently morphological filtering of the input image can be used directly.)

稀疏矩阵的实例，讲述如何用稀疏矩阵进行实际的编程。本例是以超市购物为实际背景。(Examples of sparse matrix)

光斑中心提取算法，在VS2010下，用opencv2.3.1编写的控制台程序，主要功能是利用hession矩阵和高斯滤波进行光斑中心点提取，里面附测试图片，提取精度为亚像素级别。(Spot center extraction algorithm, in VS2010, written by opencv2.3.1 console program, the main function is to use hession matrices and Gaussian filter for extracting the center spot, which is attached test images, the extraction accuracy of sub-pixel level.)

Abstract—Noninvasive ultrasound imaging of carotid plaques allows for the development of plaque-image analysis methods associated with the risk of stroke. This paper presents several plaqueimage analysis methods that have been developed over the past years. The paper begins with a review of clinical methods for visual classification that have led to standardized methods for image acquisition, describes methods for image segmentation and denoizing, and provides an overview of the several texture-feature extraction and classification methods that have been applied. We provide a summary of emerging trends in 3-D imaging methods and plaque-motion analysis. Finally, we provide a discussion of the emerging trends and future directions in our concluding remarks.