ocm4x8c3上的点阵程序,非常好的程序,我试过了,很好用,哈哈,希望对大家能有帮助,...

01 VC图像读取源代码，实现VC界面下读取各种格式图像-01 VC images read source code, VC interface formats to read images

双线性曲面、回转曲面-bilinear surface, Rotary Surface

滴玲玲验证码识别，VB代码，字模设计，PNG读取，值得一看-Identification Verification Code drops Lingling, VB code, the design matrix, PNG read, worth a visit

遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法，-Genetic Algorithm (Genetic Algorithm) is a simulation of Darwinian biological evolution, natural selection and genetic mechanism of the process of biological evolution computing model, is a simulation of natural evolutionary processes through the optimal solution search methods,

一个人脸检测的代码.用haar算法实现.-a face detection code. Haar used algorithm.

Matlab code for the implementation of the snake(active contour). Very useful in the image segmentation.

Combine Two Groups of Images把两幅图像叠放在一起 需要进行程序的分析-Combine Two Groups of Images 2 images stacked together to the need for process analysis

多小波处理程序，实现常用多小波与单小波小波分解重构，具有预滤波与后滤波功能-Multiwavelet treatment procedures commonly used Multiwavelet realize single wavelet decomposition wavelet reconstruction with pre-filtering and post filtering

应用背景 SIFT特征匹配算法, 在没有先验知识条件下, 对旋转、尺度缩放、光照、视角变化等图像变化因素保持一定鲁棒性, 且对低重叠区域(30%左右的相似重叠区域)、部分遮挡(只出现在其中一张图上的物体)和噪声等因素也保持较好的可匹配性。可用于多尺度下的特征匹配，以及鲁棒性的图像拼接。 关键技术 1.高斯尺度空间极值检测 2.特征点位置的确定 3.特征点方向的分配 4.生成SIFT特征向量 5.图像拼接 6.图像融合 提取图像的sift特征，构造特征描述子，基于sift特征构造BBF-tree，根据描述子之间的欧式距离进行初步的匹配，然后进过鲁棒的Ransac估计出最佳运动模型H，然后用距离加权的方法在重叠区进行融合。

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