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这篇blog,原来是西弗吉利亚大学的Li xin整理的,CV代码相当的全,不知道要经过多长时间的积累才会有这么丰富的资源,在此谢谢LI Xin 。我现在分享给大家,希望可以共同进步!还有,我需要说一下,不管你的理论有多么漂亮,不管你有多聪明,如果没有实验来证明,那么都是错误的。 OK~本博文未经允许,禁止转载哦! By wei shen
Reproducible Research in Computational Science
“It doesn‘t matter how beautiful your theory is, it doesn‘t matter how smart you are. If it doesn‘t agree with experiment, it‘s wrong” - Richard Feynman
"As a method for finding things out, science lives by its disdain for authority and its reliance on experimentation." - Chris Quigg
Welcome to this site about reproducible research in computational science (including signal processing, computer vision, machine learning and neural computation). This site is intended to share the source codes of the latest advances in various technical fields to the best of my knowledge. Only throughReproducible Research (RR), can we live up to the standard that hard-core science has established since Bacon and Newton. If you know of any release of the source codes that is missing from the list or any broken link, please kindly let me know.
Clustering-based Denoising using Locally Learned Dictionaries (K-LLD)
Eficient Marginal Likelihood Optimization in Blind Deconvolution code
Photorealistic Models for Pupil Light Reflex and Iridal Pattern Deformation
Distributed Gradient-Domain Processing of Planar and Spherical Images
GradientShop:A Gradient-Domain Optimization Framework for Image and Video Filtering
Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid
Digital Camera Workflow for HDR Images Using a Model of Retinal Processing
Apparent Layer Operations for the Manipulation of Deformable Objects
3d Shape Reconstruction from Photographs: a Multi-View Stereo Approach
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原文地址:http://www.cnblogs.com/molakejin/p/5148860.html