奖学金评定，由三门成绩定

贝塞尔函数的求解,result is bezier function

高斯平滑,在readme.txt里有详细的使用说明-Gaussian smoothing the readme.txt are detailed usage

用VC++实现图像分类识别、模板匹配分类器、贝叶斯分类器、线性函数分类、非线性分类、神经网络分类器

完整的DICOM医学图象浏览，可以读出DICOM图象，及其属性。并且把JPEG图象和 BMP图象转换为DICOM图象。并实现视频，AVI接口-A complete dicom image biewer, which can read the dicom image, and its properties and put the Jpg and Bmp image to Dicom format. It also realize the video and avi interface.

一个图像处理得好书，好工具-an image properly, a good tool

图象离铉变换适用与图象处理的专业人事使用-Hyun transform images from the image processing application and the use of professional personnel

该运行文件在VC6.0环境下编译通过，是一个 stand-alone 运行程序，不需要OPENCV的DLL库支持。在运行之前，请先连接好USB接口的摄像头。然后可以用鼠标选定欲跟踪目标。-document in the running VC6.0 environment compiler, is a stand-alone operating procedures, no OPENCV DLL library support. Before the operation, please connect better USB camera. The mouse can then be selected using wishing to track the target.

模拟WINDOWS系统中任务管理器的CPU使用记录的控件程序-WINDOWS System Simulation Task Manager CPU usage control procedures

Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem (QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and difficult to approximate; (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges. Four are the benefits that follow from this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity matrix; (2) The factorization allows the

应用背景 Discrete-Continuous Optimization for Multi-Target Tracking (CVPR 2012) 关键技术The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. T