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Computer Vision Resources Softwares Topic Resources References Feature Extraction SIFT [1] [Demo program][SIFT Library] [VLFeat] PCA-SIFT [2] [Project] Affine-SIFT [3] [Project] SURF [4] [OpenSURF] [Matlab Wrapper] Affine Covariant Features [5] [Oxfo…
持续更新ing~ all *.files come from the author:http://www.cnblogs.com/findumars/p/5009003.html 1 牛人Homepages(随意排序,不分先后): 1.USC Computer Vision Group:南加大,多目标跟踪/检测等: 2.ETHZ Computer Vision Laboratory:苏黎世联邦理工学院,欧洲最好的几个CV/ML研究机构: 3.Helmut Grabner:Online Boost…
From:http://rogerioferis.com/VisualRecognitionAndSearch2014/Resources.html Source Code Non-exhaustive list of state-of-the-art implementations related to visual recognition and search. There is no warranty for the source code links below – use them a…
积累记录一些视觉实验室,方便查找 1.  多伦多大学计算机科学系 2.  普林斯顿大学计算机视觉和机器人实验室 3.  牛津大学Torr Vision Group 4.  伯克利视觉和学习中心 Prof. Trevor Darrell CS280 Computer Vision Object Detection and Segmentation for RGB-D Images 5. Carnegie Mellon University(CMU) Compoter Vision Group Kr…
In the 1960s, the legendary Stanford artificial intelligence pioneer, John McCarthy, famously gave a graduate student the job of “solving” computer vision as a summer project. It has occupied an entire community of academic researchers for the past 4…
As I walked through the large poster-filled hall at CVPR 2013, I asked myself, “Quo vadis Computer Vision?" (Where are you going, computer vision?)  I see lots of papers which exploit last year’s ideas, copious amounts of incremental research, and an…
    WTF is computer vision? Posted Nov 13, 2016 by Devin Coldewey, Contributor   Next Story   Someone across the room throws you a ball and you catch it. Simple, right? Actually, this is one of the most complex processes we've ever attempted to compr…
本文把自己理解的图像存储格式总结一下. 计算机中的数据,都是二进制的,所以图片也不例外. 这是opencv文档的描述,具体在代码里面,使用矩阵来进行存储. 类似下图是(BGR格式): 图片的最小单位是像素,这里是BGR(通常我们说的blud.green.red的表示法)表示每个像素对应的值(这里BGR的混合,可以得到我们可见光的所有值). 如果是单通道(例如:灰度化之后的图像,这里就只有一列) 参考可见光光谱: 因为物体都是原子组成,原子都在运动,运动会产生光波,不同的物体生成的光波不一样,人类…
Analyzing The Papers Behind Facebook's Computer Vision Approach Introduction You know that company called Facebook? Yeah, the one that has 1.6 billion people hooked on their website. Take all of the happy birthday posts, embarrassing pictures of you…
The picture above is funny. But for me it is also one of those examples that make me sad about the outlook for AI and for Computer Vision. What would it take for a computer to understand this image as you or I do? I challenge you to think explicitly…