搜索资源列表
HC-visual-detection-by-matlab
- 本资源是清华大学程明明在2011年CVPR上发表的一篇关于视觉显著性的代码，资源包括原文及matlab版本的代码。-It is a source code of visual saliency detection proposed by M.M Cheng in cvpr2011.And it is coded with matlab.
voc-release1
- CVPR2008一篇判别训练，多尺度，变形部分建模的实现作者P. Felzenszwalb等-This is an implementation of the system described in:P. Felzenszwalb, D. McAllester, D. Ramaman.A Discriminatively Trained, Multiscale, Deformable Part Model.To appear in CVPR 2008.
matlab_OPQ_release_v1.1
- Optimized Product Quantization for Approximate Nearest Neighbor Search Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013
Multi-Target-Tracking
- 利用连续能量函数最小化进行目标值追踪。在2011年发表于cvpr-Multi-Target Tracking by Continuous Energy Minimization，In CVPR, Colorado Springs, USA, June 2011
MilTrack_matlab_v1.0
- MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Boris Babenko, Ming-Hsuan Yang, Serge Belongie ,CVPR 2009, Miami, Florida. -MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Bori
CVPR12_SAS_code
- 2012 CVPR 文章：Segmentation Using Superpixels A Bipartite Graph Partitioning Approach对应的代码-2012 CVPR paper：Segmentation Using Superpixels A Bipartite Graph Partitioning Approach
DeHaze
- 根据CVPR最佳论文实现的基于暗通道的图像去雾算法（含soft matting），以及算法改进：包括对色彩失真的改进以及一种达到soft matting效果的快速算法-According CVPR Best Paper realized based on dark channel images to fog algorithms (including soft matting), and algorithm improvements: Improvements include color dis
hoof--cvpr09
- R. Chaudhry, A. Ravichandran, G. Hager and R. Vidal Histograms of Oriented Optical Flow and Binet-Cauchy Kernels on Nonlinear Dynamical Systems for the Recognition of Human Actions CVPR 2009 (c) Rizwan Chaudhry - JHU Vision Lab-R. Chaudhry,
stereo
- 做图切割 2013最新cvpr文章 效果比较好 值得下载-Graphing latest cutting 2013
OpenSourceVidProcessing
- 光流法实时实现的文献及代码，2012发表于cvpr，200*200的图像，每秒可达200帧，2012_CVPR_Lip-motion events analysis and lip segmentation using optical flow-Optical flow method to achieve real-time literature and code, 2012 published in cvpr, 200* 200 images per second, up to 200, 20
demo_mtjsrc_OxfordFlower_17
- 此程序为袁晓彤CVPR论文“基于多任务联合稀疏表示的的视觉分类”的主代码部分-The program for Yuan Xiaotong CVPR paper " visual classification with Muti-task joint sparse representation" main code of the parts
Image-Classification
- 本文实现了09年CVPR的文章Linear Spatial Pyramid Matching using Sparse Coding for Image Classification-This package contains the Matlab codes implementing the ScSPM algorithm described in CVPR 09 paper "Linear Spatial Pyramid Matching using Sparse Coding for
FastMatch_v1
- "Fast-Match: Fast Affine Template Matching" cvpr2013Fast_Match, title={Fast-Match: Fast Affine Template Matching}, author={Korman, Simon and Reichman, Daniel and Tsur, Gilad and Avidan, Shai}, booktitle={Computer Vision and Pattern Recogni
High-accuracy-Optical-Flow
- High accuracy optical flow using a theory for warping-The files optical_flow_brox.m and optical_flow_sand.m are the files you need to run. optical_flow_brox.m implements the traditional algorithm. Just replace the first lines where images are
evaluation
- 2012年cvpr上面的一篇论文的研究结果，实现的是多目标跟踪的离散连续优化。-2012 cvpr paper above findings, the realization of the multi-target tracking discrete continuous optimization.
Fast--Accurate-Detection-
- CVPR 2013 best oral paper
Mei_Segment-Tree_Based_Cost
- s世界计算机视觉三大会议之CVPR会议，中国人发表的经典立体匹配的论文-s World Computer Vision Conference CVPR three meetings, the Chinese classic stereo matching published papers
SiftsAndTheirScales
- CVPR2012上关于SIFT及其尺度的文章及源代友，对于研究SIFT与尺度空间的同学值得一看-code and paper about SIFT and their scales of CVPR 2012, of great value to guys focusing on image processing
gridCut
- graphcut的最新改进，在网格上应用graphcut，作者将其称为gridCut,此压缩包内含文章与源代码，出自CVPR2012,关注计算机视觉的同学可以详细参考，代码已调试通过-the new advance of graphcut, named gridCut, derived from CVPR 2012, useful to guys on ComputerVision.The code is runnable.
dksvd
- DK-SVD，CVPR文章 Discriminative K-SVD dictionary learning for face recognition 源码，效果好于原始SRC-DK-SVD，the source code of CVPR paper Discriminative K-SVD dictionary learning for face recognition, its has a better performance than the classical SRC classifie