资源列表
src_vc
- c语言关于灰度变换的一些源码，效果很好，需要的朋友下载-c language on some of the source of the gray scale transform, the effect is very good, need a friend to download
ScanPolygon
- 利用mfc画多边形，并且能实现填充功能，不是扫描线填充，而是自动填充-Using mfc draw polygons, and can achieve filling function, not the scan line fill, but the auto-fill
MKL
- 多核学习 SVM分类 遥感影像分类算法 -Multiple kernel learning
image-feature-detection-and-matching
- 用于图像特征提取和匹配，三维重建等的经典方法，包括sift，surf，Harris，RANSAC,8点算法等-For image feature extraction and matching, 3D reconstruction and other classical methods, including sift, surf, Harris, RANSAC, 8 point algorithm, etc.
ComputerVision
- 图像图形处理图书，《计算机视觉-计算理论与算法基础》。-Image graphics library, " Computer Vision- Computational Theory and algorithm based."
NLR_MRF
- 非局部马尔科夫随机场，用于自然图像的去噪，效果非常好，推荐大家使用(Non local Markov random field, for natural image denoising, the effect is very good, recommended for you to use)
ColorProcess
- 基于MFC对话框编写，对彩色图像进行红绿蓝三种颜色的滤除，并具有Gama矫正功能-MFC dialog based on the preparation of the color images in three colors red, green and blue filter, and has Gama correction function
digital-image-processing
- digital image processing 图像处理课件，是我们上课教授讲的，把讲到的发上来，希望对大家有用。-digital image processing image processing courseware, is the professor talking about school, talked about the hair to come, we hope to be useful.
KinectDemo
- 基于kinect的一个综合应用，其中包含了基于颜色的目标识别，kinect平台的俯仰程序，手势识别，骨骼识别，具有较好实用性。-Based on a comprehensive application kinect, which contains a color-based object recognition, kinect platform pitching program, gesture recognition, bones identified, with good usability
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- 图像去雾可以还原图像的颜色和能见度，同时也能利用雾的浓度来估计物体的距离，这些在计算机视觉上都有重要应用(Image Fog can restore the color and visibility of the image, but also can use the concentration of the fog to estimate the distance of the object, which have important applications in computer visi
ImgaeFusionWebsite
- ImgaeFusionWebsite网上找的一些有关图像融合的相关资料-Some relevant information about the image fusion ImgaeFusionWebsite find online
robust-PCA
- robust PCA的应用实例 很有代表性的方法-this paper proposed a approach of robust face recognition by exploiting the sparse error component obtained by RPCA.