实现图像数据的JPEG

创建指定的图像对象的直方图数据。如果未给定坐标，则指向整个图像。-Images to create the specified data object histogram. If you are not given the coordinates, then point to the entire image.

LCD driver to display font

基于边缘检测的细胞图像分割方法研究与实现-Based on Edge Detection of cell image segmentation research and realize

非常详细的介绍了计算机视觉基础知识-摄像机模型，内参数，外参数和射影几何的知识，简单易懂-Very detailed introduction of the basic knowledge of computer vision- the camera model, with parameters, outside the parameters and the projective geometry of knowledge, easy-to-read

通过CCD拍照后分析所拍图象的坐标以及相似度。-Through the CCD camera image after analyzing the film as well as the similarity of the coordinates.

首先通过多分辨率分解法提取高频边缘，然后分别对每层的高频边缘进行非线性插值得到新的高频边缘，接着对高频边缘进行高通滤波得到等强后的高频边缘，最后对增强后的高频边缘进行修正。再在对增强的图像进行对比度拉伸，从而达到增强的目的！-First of all, through the extraction of multi-resolution decomposition of high-frequency edge, and then were on the edge of each high-frequency non-linear interpolation to be the new high-frequency edge, and then the edge of the high-frequency high-pass filter to be strong, such as after the high-frequency edge, and finally on the brink of enhanced high-frequency be amended. Again in contrast enhanced image stretching, so as to achieve the purpose of enhancing!

计算机图形学部分算法vc原码，实现了常用的功能-Computer graphics part of the original code vc algorithm to realize the functions of commonly used

图像模式识别―VC++技术实现，杨淑莹编著，可实现图像聚类-Image Pattern Recognition-VC++ Technology, edited by杨淑莹, achievable image clustering

完整的matlab6.5图形图像处理源程序，希望对大家有用-Complete matlab6.5 graphics image processing source code, in the hope that useful to everybody

SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 -a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images.