计算机视觉一些代码
2014-06-13 10:10
85 查看
Jia-Bin Huang同学收集了很多计算机视觉方面的代码,链接如下:
https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
这些代码很实用,可以让我们站在巨人的肩膀上~~
Useful Links(dataset, lectures, and other softwares)
ECCV 2012
papers on the web 已经发布了。今天浏览了一下文章列表,找出了自己感兴趣的一些论文。那个列表目前还没有公布论文的下载链接。先把列表记下来,慢慢整理链接把。
Depth Matters: Influence of Depth Cues on Visual SaliencyLang Congyan (Beijijng Jiaotong University), Tam Nguyen (NUS - Singapore),harish Katti (National University of Singapore), Karthik Yadati (National University of Singapore), Shuicheng Yan, Mohan Kankanhalli (National University
of Singapore)
Quaternion-based Spectral Saliency Detection for Human Eye Fixation Point Prediction [bibtex]
[code #1 - saliency - will be updated soon(ish)] [code
#2 - Matlab AUC measure implementation]Boris Schauerte (Karlsruhe Inst. Tech.), Rainer Stiefelhagen (KarlsruheInst. of Technology)
Geodesic Saliency Using Background PriorsYichen Wei (Microsoft Research), Fang Wen, Wangjiang Zhu (Tsinghua University), Jian Sun (Microsoft Research Asia)
Saliency Modeling from Image HistogramsShijian Lu (I2R - A*STAR), Joo-Hwee Lim (Institute for Infocomm Research)
Salient Object Detection: A BenchmarkAli Borji (University of Southern Califor), Dicky Sihite (University of Southern California), Laurent Itti (University of Southern California)
Online Learned Discriminative Part-Based Appearance Models forMulti-Human TrackingBo Yang (USC), Ram Nevatia
Real-Time Camera Tracking: When is High Frame-Rate Best?Ankur Handa (Imperial College London), Richard Newcombe (Imperial CollegeLondon), Adrien Angeli, Andrew Davison (Imperial College London)
Online Learning of Linear Predictors for Real-Time TrackingStefan Holzer (Technische Universität München), Marc Pollefeys,Slobodan Ilic (TUM), David Joseph Tan (Technische Universität München), Nassir Navab (Technische Universität München)
Tracking Using Motion Patterns for Very Crowded ScenesXuemei Zhao (Univ. of Southern California), Dian Gong (Univ. of Southern California), Gerard Medioni (University of Southern California)
Divergence-free motion estimationDominque BÈrÈziat (UPMC), Isabelle Herlin (INRIA), Nicolas Mercier (INRIA),Sergiy Zhuk (CWI)
Coherent Filtering: Detecting Coherent Motions from CluttersBolei Zhou (The Chinese University of HK), Xiaogang Wang (The ChineseUniversity of HK), Xiaoou Tang
Statistical Inference of Motion in the InvisibleHaroon Idrees (UCF), Imran Saleemi (UCF), Mubarak Shah (UCF)
Group Tracking: Exploring Mutual Relations for Multiple Object TrackingGenquan Duan (Tsinghua University), Song Cao (Tsinghua University),Haizhou Ai (Tsinghua University), Shihong Lao (Omron Company)
Stixels motion estimation without optical flow computationBertan G¸nyel (KU Leuven), Rodrigo Benenson (KU Leuven), Radu Timofte (KULeuven), Luc Van Gool (KU Leuven)
Simultaneous Compaction and Factorization of Sparse Image Motion MatricesSusanna Ricco (Duke University), Carlo Tomasi
Efficient Nonlocal Regularization for Optical FlowPhilipp Krähenb¸hl (Stanford University), Vladlen Koltun (Stanford University)
Scale Invariant Optical FlowLi Xu (CUHK), Zhenlong Dai (CUHK), jiaya Jia (CUHK)
A Naturalistic Open Source Movie for Optical Flow EvaluationDaniel Butler (University of Washington), Jonas Wulff (Max Planck Institute for Intelligent Systems), Garrett Stanley (Department of Biomedical Engineering - Georgia Institute of Technology), Michael Black (Max Planck Institute
for Intelligent Systems)
Dynamic Context for Tracking Behind OcclusionsFei Xiong (Northeastern University), Octavia Camps (Northeastern University), Mario Sznaier (Northeastern University)
Video Matting Using Multi-Frame Nonlocal Matting LaplacainInchang Choi (KAIST), Yu-Wing Tai (KAIST), Minhaeng Lee (KAIST)
Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing DataQuanyi Mo (Colorado State University), Bruce Draper (Colorado StateUniversity)
Multi-Scale Clustering of Frame-to-Frame Correspondences for Motion SegmentationRalf Dragon (Leibniz Universit\auml),t Hannover, Bodo Rosenhahn, Joern Ostermann
Learning to segment a video to clips based on scene and camera motionAdarsh Kowdle (Cornell University), Tsuhan Chen (Cornell University)
Efficient Articulated Trajectory Reconstruction using Dynamic Programming and FiltersJack Valmadre (CSIRO), Yingying Zhu (Csiro), Sridha Sridharan (Queensland University of Technology), Simon Lucey (CSIRO)
Background Inpainting for Videos with Dynamic Objects and a Free-moving CameraMiguel Granados (MPI Informatik), Kwang In Kim (MPI for Informatics), James Tompkin (UCL), Jan Kautz (UCL), Christian Theobalt (MPI Informatik)
Active Frame Selection for Label Propagation in VideosSudheendra Vijayanarasimhan, Kristen Grauman
Streaming Hierarchical Video SegmentationChenliang Xu (SUNY at Buffalo), Caiming Xiong (SUNY at Buffalo), Jason Corso (SUNY at Buffalo)
Modeling Complex Temporal Composition of Actionlets for ActivityPredictionKang Li, Jie Hu (State University of New York (SUNY) at Buffalo), YunFu (SUNY at Buffalo)
Combining Per-Frame and Per-Track Cues for Multi-Person ActionRecognitionSameh Khamis (University of Maryland), Vlad Morariu (University of Maryland), Larry Davis (University of Maryland)
Script Data for Attribute-based Recognition of Composite ActivitiesMarcus Rohrbach (MPI Informatics), Michaela Regneri (Saarland University), Mykhaylo Andriluka (MPI Informatik), Sikandar Amin (Max-Planck - TU Munich),Manfred Pinkal, Bernt Schiele
A Unified Framework for Multi-Target Tracking and Collective ActivityRecognitionWongun Choi (The University of Michigan), Silvio Savarese (The University of Michigan - Ann Arbor)
Activity ForecastingKris Kitani (Carnegie Mellon University), James Bagnell, Martial Hebert
Propagative Hough Voting for Human Activity RecognitionGang YU (NTU), Junsong Yuan (NTU), Zicheng Liu (MSR)
Human Actions as Stochastic Kronecker GraphsSinisa Todorovic (Oregon State University)
Trajectory-Based Modeling of Human Actions with Motion Reference PointsYu-Gang Jiang (Fudan University), Qi Dai (Fudan University), XiangyangXue (Fudan University), Wei Liu (Columbia University), Chong-Wah Ngo (CityUniversity of Hong Kong)
Team Activity Recognition in SportsCem Direkoglu (Dublin City University), Noel O’Connor (Dublin City University)
Real–Time Human Pose Tracking using Range CamerasVarun Ganapathi (Google), Christian Plagemann (Google Research), DaphneKoller (Stanford University), Sebastian Thrun (Google)
Object Co-detectionYinzge Bao (U of Michigan at Ann Arbor), Yu Xiang (University of Michigan), Silvio Savarese (The University of Michigan - Ann Arbor)
Hausdorff Distance Constraint for Multi-Surface SegmentationFrank Schmidt (ESIEE), Yuri Boykov (University of Western Ontario)
Background Subtraction using Group Sparsity and Low Rank constraintXinyi Cui (Rutgers University), Junzhou Huang, shaoting Zhang (Rutgers University), Dimitris Metaxas (Rutgers University)
Shape Sharing for Object SegmentationJaechul Kim (University of Texas at Austin), Kristen Grauman
On Learning Higher-Order Consistency Potentials for Multi-class Pixel LabelingKyoungup Park (ANU), Stephen Gould (ANU)
Object detection using strongly-supervised deformable part modelsHossein Azizpour (KTH), Ivan Laptev
Hough Regions for Joining Instance Localization and SegmentationHayko Riemenschneider (Graz University of Technology), Sabine Sternig (Graz University of Technology), Michael Donoser (Graz University ofTechnology), Peter Roth (Graz University of Technology)
Latent Hough Transform for Object DetectionNima Razavi (ETH Zurich), Juergen Gall (ETH Zurich), Pushmeet Kohli, LucVan Gool
Annotation Propagation in Large Image Databases via Dense Image CorrespondenceMichael Rubinstein (MIT), Ce Liu (Microsoft Research New England), WilliamFreeman (Massachusetts Institute of Technology)
Fast Tiered Labeling with Topological PriorsYing Zheng (Duke University - Computer Science), Steve Gu (DukeUniversity - Computer Scie), Carlo Tomasi
Multi-Component Models for Object DetectionChunhui Gu (UC Berkeley), Pablo Arbelaez (UC Berkeley), Yuanqing Lin (NECLaboratories Amertica), Kai Yu (NEC Laboratories Amertica), Jitendra Malik (UCBerkeley)
Joint Classification-Regression Forests for Spatially Structured Multi-Object SegmentationBen Glocker (Microsoft Research Cambridge), Olivier Pauly (TechnischeUniversitaet Muenchen), Ender Konukoglu (Microsoft Research Cambridge), AntonioCriminisi (Microsoft Research Cambridge)
Using linking features in learning non-parametric part modelsLeonid Karlinsky (Weizmann Institute of Science), Shimon Ullman (WeizmannInstitute of Science)
Connecting Missing Links: Object Discovery from Sparse ObservationsHongwen Kang (Carnegie Mellon University), Martial Hebert, Takeo Kanade
Beyond the line of sight: labeling the underlying surfacesRuiqi Guo (UIUC), Derek Hoiem (University of Illinois)
Optimal Templates for Non-Rigid Surface ReconstructionMarkus Moll (K.U.Leuven), Luc Van Gool
Scale Robust Multi View StereoChristian Bailer, Manuel Finckh (Tuebingen University), Hendrik Lensch (Tuebingen University)
Multiple View Object Cosegmentation using Appearance and Stereo CuesAdarsh Kowdle (Cornell University), Sudipta Sinha, Rick Szeliski
Detection of Independently Moving Objects in Non-planar Scenes via Multi-Frame Monocular Epipolar ConstraintVladimir Reilly (University of Central Florida), Soumyabrata Dey (University of Central Florida), Mubarak Shah (UCF)
3D Reconstruction of Dynamic Scenes with Multiple Handheld CamerasHanqing Jiang (Zhejiang University), Haomin Liu (Zhejiang University), PingTan (National University of Singapore), Guofeng Zhang (Zhejiang University),Hujun Bao (Zhejiang University)
Auto-grouped Sparse Representation for Visual AnalysisJiashi Feng (NUS), Xiaotong Yuan, zilei Wang, Huan Xu, Shuicheng Yan
Undoing the Damage of Dataset BiasAditya Khosla (MIT), Tinghui Zhou (CMU), Tomasz Malisiewicz (MIT), Alyosha Efros (CMU), Antonio Torralba (MIT)
Unsupervised Discovery of Mid-Level Discriminative PatchesSaurabh Singh (Carnegie Mellon University), Abhinav Gupta, Alyosha Efros (CMU)
A new biologically inspired color- and shape-based image descriptorJun Zhang (Brown University), Youssef Barhomi (Brown University), ThomasSerre (Brown University)
Continuous Regression for Non-Rigid Image AlignmentEnrique Sanchez Lozano (Gradiant), Fernando De la Torre (Carnegie Mellon University), Daniel Gonzalez Jimenez (Gradiant)
Coregistration: Simultaneous Alignment and Modeling of Articulated 3D ShapeDavid Hirshberg (MPI for Intelligent Systems), Matthew Loper (MPI for Intelligent Systems), Eric Rachlin (MPI for Intelligent Systems), MichaelBlack (Max Planck Institute for Intelligent Systems)
Discovering Latent Domains for Multisource Domain AdaptationJudy Hoffman (UC Berkeley), Kate Saenko (UC Berkeley - Harvard - ICSI),Brian Kulis (Ohio State), Trevor Darrell (UC Berkeley - ICSI)
https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
这些代码很实用,可以让我们站在巨人的肩膀上~~
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] [Oxford project] MSER [6] [Oxford project] [VLFeat] Geometric Blur [7] [Code] Local Self-Similarity Descriptor [8] [Oxford implementation] Global and Efficient Self-Similarity [9] [Code] Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows] GIST [11] [Project] Shape Context [12] [Project] Color Descriptor [13] [Project] Pyramids of Histograms of Oriented Gradients [Code] Space-Time Interest Points (STIP) [14] [Code] Boundary Preserving Dense Local Regions [15][Project] | 1. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. [PDF] 2. Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. [PDF] 3. J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009. [PDF] 4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features,ECCV, 2006. [PDF] 5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, A comparison of affine region detectors. IJCV, 2005. [PDF] 6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002. [PDF] 7. A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005. [PDF] 8. E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007. [PDF] 9. T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010. [PDF] 10. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF] 11. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001. [PDF] 12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002. [PDF] 13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010. 14. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF] 15. J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011. [PDF] |
Image Segmentation | · Normalized Cut [1] [Matlab code] · Gerg Mori' Superpixel code [2] [Matlab code] · Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper] · Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper] · OWT-UCM Hierarchical Segmentation [5] [Resources] · Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code] · Quick-Shift [7] [VLFeat] · SLIC Superpixels [8] [Project] · Segmentation by Minimum Code Length [9] [Project] · Biased Normalized Cut [10] [Project] · Segmentation Tree [11-12] [Project] · Entropy Rate Superpixel Segmentation [13] [Code] | 1. J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 [PDF] 2. X. Ren and J. Malik. Learning a classification model for segmentation.ICCV, 2003. [PDF] 3. P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004. [PDF] 4. D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002. [PDF] 5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011. [PDF] 6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009. [PDF] 7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking,ECCV, 2008. [PDF] 8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010. [PDF] 9. A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007. [PDF] 10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 11. E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009. [PDF] 12. N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 [PDF] 13. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 [PDF] |
Object Detection | · A simple object detector with boosting [Project] · INRIA Object Detection and Localization Toolkit [1] [Project] · Discriminatively Trained Deformable Part Models [2] [Project] · Cascade Object Detection with Deformable Part Models [3] [Project] · Poselet [4] [Project] · Implicit Shape Model [5] [Project] · Viola and Jones's Face Detection [6] [Project] | 1. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005. [PDF] 2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 [PDF] 3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR 2010 [PDF] 4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 [PDF] 5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008. [PDF] 6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR 2001. [PDF] |
Saliency Detection | · Itti, Koch, and Niebur' saliency detection [1] [Matlab code] · Frequency-tuned salient region detection [2] [Project] · Saliency detection using maximum symmetric surround [3] [Project] · Attention via Information Maximization [4] [Matlab code] · Context-aware saliency detection [5] [Matlab code] · Graph-based visual saliency [6] [Matlab code] · Saliency detection: A spectral residual approach. [7] [Matlab code] · Segmenting salient objects from images and videos. [8] [Matlab code] · Saliency Using Natural statistics. [9] [Matlab code] · Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code] · Learning to Predict Where Humans Look [11] [Project] · Global Contrast based Salient Region Detection [12] [Project] | 1. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998. [PDF] 2. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. [PDF] 3. R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010. [PDF] 4. N. Bruce and J. Tsotsos. Saliency based on information maximization. InNIPS, 2005. [PDF] 5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. [PDF] 6. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. [PDF] 7. X. Hou and L. Zhang. Saliency detection: A spectral residual approach.CVPR, 2007. [PDF] 8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010. [PDF] 9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008. [PDF] 10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004. [PDF] 11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009. [PDF] 12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR 2011. |
Image Classification | · Pyramid Match [1] [Project] · Spatial Pyramid Matching [2] [Code] · Locality-constrained Linear Coding [3] [Project] [Matlab code] · Sparse Coding [4] [Project] [Matlab code] · Texture Classification [5] [Project] · Multiple Kernels for Image Classification [6] [Project] · Feature Combination [7] [Project] · SuperParsing [Code] | 1. K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005. [PDF] 2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006[PDF] 3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 [PDF] 4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 [PDF] 5. M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. [PDF] 6. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009. [PDF] 7. P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. [PDF] 8. J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010. [PDF] |
Category-Independent Object Proposal | · Objectness measure [1] [Code] · Parametric min-cut [2] [Project] · Object proposal [3] [Project] | 1. B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 [PDF] 2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010. [PDF] 3. I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. [PDF] |
MRF | · Graph Cut [Project] [C++/Matlab Wrapper Code] | 1. Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF] |
Shadow Detection | · Shadow Detection using Paired Region [Project] · Ground shadow detection [Project] | 1. R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 [PDF] 2. J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 [PDF] |
Optical Flow | · Kanade-Lucas-Tomasi Feature Tracker [C Code] · Optical Flow Matlab/C++ code by Ce Liu [Project] · Horn and Schunck's method by Deqing Sun [Code] · Black and Anandan's method by Deqing Sun [Code] · Optical flow code by Deqing Sun [Matlab Code] [Project] · Large Displacement Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows] [Project] · Variational Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Executable for 32-bit Windows ] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows ] [Project] | 1. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF] 2. J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF] 3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. MIT 2009. [PDF] 4. B.K.P. Horn and B.G. Schunck, Determining Optical Flow, Artificial Intelligence 1981. [PDF] 5. M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF] 6. D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, CVPR 2010. [PDF] 7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI, 2010 [PDF] 8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 [PDF] |
Object Tracking | · Particle filter object tracking [1] [Project] · KLT Tracker [2-3] [Project] · MILTrack [4] [Code] · Incremental Learning for Robust Visual Tracking [5] [Project] · Online Boosting Trackers [6-7] [Project] · L1 Tracking [8] [Matlab code] | 1. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV, 2002. [PDF] 2. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, IJCAI 1981. [PDF] 3. J. Shi, C. Tomasi, Good Feature to Track, CVPR 1994. [PDF] 4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, PAMI 2011 [PDF] 5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 [PDF] 6. H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF] 7. H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking, ECCV 2008 [PDF] 8. X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009. [PDF] |
Image Matting | · Closed Form Matting [Code] · Spectral Matting [Project] · Learning-based Matting [Code] | 1. A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008 [PDF] 2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008. [PDF] 3. Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 [PDF] |
Bilateral Filtering | · Fast Bilateral Filter [Project] · Real-time O(1) Bilateral Filtering [Code] · SVM for Edge-Preserving Filtering [Code] | 1. Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009. [PDF] 2. Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010. [PDF] |
Image Denoising | · K-SVD [Matlab code] · BLS-GSM [Project] · BM3D [Project] · FoE [Code] · GFoE [Code] · Non-local means [Code] · Kernel regression [Code] | |
Image Super-Resolution | · MRF for image super-resolution [Project] · Multi-frame image super-resolution [Project] · UCSC Super-resolution [Project] · Sprarse coding super-resolution [Code] | |
Image Deblurring | · Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code] · Analyzing spatially varying blur [Project] · Radon Transform [Code] | |
Image Quality Assessment | · FSIM [1] [Project] · Degradation Model [2] [Project] · SSIM [3] [Project] · SPIQA [Code] | 1. L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment, TIP 2011. [PDF] 2. N. Damera-Venkata, and T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik,Image Quality Assessment Based on a Degradation Model, TIP 2000. [PDF] 3. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF] 4. B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA), ICIP 2008. [PDF] |
Density Estimation | · Kernel Density Estimation Toolbox [Project] | |
Dimension Reduction | · Dimensionality Reduction Toolbox [Project] · ISOMAP [Code] · LLE [Project] · LaplacianEigenmaps [Code] · Diffusion maps [Code] | |
Sparse Coding | ||
Low-Rank Matrix Completion | ||
Nearest Neighbors matching | · ANN: Approximate Nearest Neighbor Searching [Project] [Matlab wrapper] · FLANN: Fast Library for Approximate Nearest Neighbors [Project] | |
Steoreo | · StereoMatcher [Project] | 1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2002 [PDF] |
Structure from motion | · Boundler [1] [Project] | 1. N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH, 2006. [PDF] |
Distance Transformation | · Distance Transforms of Sampled Functions [1] [Project] | 1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Technical report, Cornell University, 2004. [PDF] |
Chamfer Matching | · Fast Directional Chamfer Matching [Code] | 1. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching, CVPR 2010 [PDF] |
Clustering | · K-Means [VLFeat] [Oxford code] · Spectral Clustering [UW Project][Code] [Self-Tuning code] · Affinity Propagation [Project] | |
Classification | · SVM [Libsvm] [SVM-Light] [SVM-Struct] · Boosting · Naive Bayes | |
Regression | · SVM · RVM · GPR | |
Multiple Kernel Learning (MKL) | · SHOGUN [Project] · OpenKernel.org [Project] · DOGMA (online algorithms) [Project] · SimpleMKL [Project] | 1. S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006. [PDF] 2. F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011. [PDF] 3. F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010. [PDF] 4. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008. [PDF] |
Multiple Instance Learning (MIL) | · MIForests [1] [Project] · MILIS [2] · MILES [3] [Project] [Code] · DD-SVM [4] [Project] | 1. C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010. [PDF] 2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010. [PDF] 3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 [PDF] 4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004. [PDF] |
Other Utilities | · Code for downloading Flickr images, by James Hays [Code] · The LightspeedMatlab Toolbox by Tom Minka [Code] · MATLAB Functions for Multiple View Geometry [Code] · Peter's Functions for Computer Vision [Code] · Statistical Pattern Recognition Toolbox [Code] |
Conference Information | · Computer Image Analysis, Computer Vision Conferences |
Papers | · Computer vision paper on the web · NIPS Proceedings |
Datasets | · Compiled list of recognition datasets · Computer vision dataset from CMU |
Lectures | · Videolectures |
Source Codes | · Computer Vision Algorithm Implementations · OpenCV · Source Code Collection for Reproducible Research |
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
papers on the web 已经发布了。今天浏览了一下文章列表,找出了自己感兴趣的一些论文。那个列表目前还没有公布论文的下载链接。先把列表记下来,慢慢整理链接把。
显著性相关
Depth Matters: Influence of Depth Cues on Visual SaliencyLang Congyan (Beijijng Jiaotong University), Tam Nguyen (NUS - Singapore),harish Katti (National University of Singapore), Karthik Yadati (National University of Singapore), Shuicheng Yan, Mohan Kankanhalli (National Universityof Singapore)
Quaternion-based Spectral Saliency Detection for Human Eye Fixation Point Prediction [bibtex]
[code #1 - saliency - will be updated soon(ish)] [code
#2 - Matlab AUC measure implementation]Boris Schauerte (Karlsruhe Inst. Tech.), Rainer Stiefelhagen (KarlsruheInst. of Technology)
Geodesic Saliency Using Background PriorsYichen Wei (Microsoft Research), Fang Wen, Wangjiang Zhu (Tsinghua University), Jian Sun (Microsoft Research Asia)
Saliency Modeling from Image HistogramsShijian Lu (I2R - A*STAR), Joo-Hwee Lim (Institute for Infocomm Research)
Salient Object Detection: A BenchmarkAli Borji (University of Southern Califor), Dicky Sihite (University of Southern California), Laurent Itti (University of Southern California)
跟踪和光流
Online Learned Discriminative Part-Based Appearance Models forMulti-Human TrackingBo Yang (USC), Ram NevatiaReal-Time Camera Tracking: When is High Frame-Rate Best?Ankur Handa (Imperial College London), Richard Newcombe (Imperial CollegeLondon), Adrien Angeli, Andrew Davison (Imperial College London)
Online Learning of Linear Predictors for Real-Time TrackingStefan Holzer (Technische Universität München), Marc Pollefeys,Slobodan Ilic (TUM), David Joseph Tan (Technische Universität München), Nassir Navab (Technische Universität München)
Tracking Using Motion Patterns for Very Crowded ScenesXuemei Zhao (Univ. of Southern California), Dian Gong (Univ. of Southern California), Gerard Medioni (University of Southern California)
Divergence-free motion estimationDominque BÈrÈziat (UPMC), Isabelle Herlin (INRIA), Nicolas Mercier (INRIA),Sergiy Zhuk (CWI)
Coherent Filtering: Detecting Coherent Motions from CluttersBolei Zhou (The Chinese University of HK), Xiaogang Wang (The ChineseUniversity of HK), Xiaoou Tang
Statistical Inference of Motion in the InvisibleHaroon Idrees (UCF), Imran Saleemi (UCF), Mubarak Shah (UCF)
Group Tracking: Exploring Mutual Relations for Multiple Object TrackingGenquan Duan (Tsinghua University), Song Cao (Tsinghua University),Haizhou Ai (Tsinghua University), Shihong Lao (Omron Company)
Stixels motion estimation without optical flow computationBertan G¸nyel (KU Leuven), Rodrigo Benenson (KU Leuven), Radu Timofte (KULeuven), Luc Van Gool (KU Leuven)
Simultaneous Compaction and Factorization of Sparse Image Motion MatricesSusanna Ricco (Duke University), Carlo Tomasi
Efficient Nonlocal Regularization for Optical FlowPhilipp Krähenb¸hl (Stanford University), Vladlen Koltun (Stanford University)
Scale Invariant Optical FlowLi Xu (CUHK), Zhenlong Dai (CUHK), jiaya Jia (CUHK)
A Naturalistic Open Source Movie for Optical Flow EvaluationDaniel Butler (University of Washington), Jonas Wulff (Max Planck Institute for Intelligent Systems), Garrett Stanley (Department of Biomedical Engineering - Georgia Institute of Technology), Michael Black (Max Planck Institute
for Intelligent Systems)
Dynamic Context for Tracking Behind OcclusionsFei Xiong (Northeastern University), Octavia Camps (Northeastern University), Mario Sznaier (Northeastern University)
运动和视频分割
Video Matting Using Multi-Frame Nonlocal Matting LaplacainInchang Choi (KAIST), Yu-Wing Tai (KAIST), Minhaeng Lee (KAIST)Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing DataQuanyi Mo (Colorado State University), Bruce Draper (Colorado StateUniversity)
Multi-Scale Clustering of Frame-to-Frame Correspondences for Motion SegmentationRalf Dragon (Leibniz Universit\auml),t Hannover, Bodo Rosenhahn, Joern Ostermann
Learning to segment a video to clips based on scene and camera motionAdarsh Kowdle (Cornell University), Tsuhan Chen (Cornell University)
Efficient Articulated Trajectory Reconstruction using Dynamic Programming and FiltersJack Valmadre (CSIRO), Yingying Zhu (Csiro), Sridha Sridharan (Queensland University of Technology), Simon Lucey (CSIRO)
Background Inpainting for Videos with Dynamic Objects and a Free-moving CameraMiguel Granados (MPI Informatik), Kwang In Kim (MPI for Informatics), James Tompkin (UCL), Jan Kautz (UCL), Christian Theobalt (MPI Informatik)
Active Frame Selection for Label Propagation in VideosSudheendra Vijayanarasimhan, Kristen Grauman
Streaming Hierarchical Video SegmentationChenliang Xu (SUNY at Buffalo), Caiming Xiong (SUNY at Buffalo), Jason Corso (SUNY at Buffalo)
行为识别
Modeling Complex Temporal Composition of Actionlets for ActivityPredictionKang Li, Jie Hu (State University of New York (SUNY) at Buffalo), YunFu (SUNY at Buffalo)Combining Per-Frame and Per-Track Cues for Multi-Person ActionRecognitionSameh Khamis (University of Maryland), Vlad Morariu (University of Maryland), Larry Davis (University of Maryland)
Script Data for Attribute-based Recognition of Composite ActivitiesMarcus Rohrbach (MPI Informatics), Michaela Regneri (Saarland University), Mykhaylo Andriluka (MPI Informatik), Sikandar Amin (Max-Planck - TU Munich),Manfred Pinkal, Bernt Schiele
A Unified Framework for Multi-Target Tracking and Collective ActivityRecognitionWongun Choi (The University of Michigan), Silvio Savarese (The University of Michigan - Ann Arbor)
Activity ForecastingKris Kitani (Carnegie Mellon University), James Bagnell, Martial Hebert
Propagative Hough Voting for Human Activity RecognitionGang YU (NTU), Junsong Yuan (NTU), Zicheng Liu (MSR)
Human Actions as Stochastic Kronecker GraphsSinisa Todorovic (Oregon State University)
Trajectory-Based Modeling of Human Actions with Motion Reference PointsYu-Gang Jiang (Fudan University), Qi Dai (Fudan University), XiangyangXue (Fudan University), Wei Liu (Columbia University), Chong-Wah Ngo (CityUniversity of Hong Kong)
Team Activity Recognition in SportsCem Direkoglu (Dublin City University), Noel O’Connor (Dublin City University)
Real–Time Human Pose Tracking using Range CamerasVarun Ganapathi (Google), Christian Plagemann (Google Research), DaphneKoller (Stanford University), Sebastian Thrun (Google)
目标检测与分割
Object Co-detectionYinzge Bao (U of Michigan at Ann Arbor), Yu Xiang (University of Michigan), Silvio Savarese (The University of Michigan - Ann Arbor)Hausdorff Distance Constraint for Multi-Surface SegmentationFrank Schmidt (ESIEE), Yuri Boykov (University of Western Ontario)
Background Subtraction using Group Sparsity and Low Rank constraintXinyi Cui (Rutgers University), Junzhou Huang, shaoting Zhang (Rutgers University), Dimitris Metaxas (Rutgers University)
Shape Sharing for Object SegmentationJaechul Kim (University of Texas at Austin), Kristen Grauman
On Learning Higher-Order Consistency Potentials for Multi-class Pixel LabelingKyoungup Park (ANU), Stephen Gould (ANU)
Object detection using strongly-supervised deformable part modelsHossein Azizpour (KTH), Ivan Laptev
Hough Regions for Joining Instance Localization and SegmentationHayko Riemenschneider (Graz University of Technology), Sabine Sternig (Graz University of Technology), Michael Donoser (Graz University ofTechnology), Peter Roth (Graz University of Technology)
Latent Hough Transform for Object DetectionNima Razavi (ETH Zurich), Juergen Gall (ETH Zurich), Pushmeet Kohli, LucVan Gool
Annotation Propagation in Large Image Databases via Dense Image CorrespondenceMichael Rubinstein (MIT), Ce Liu (Microsoft Research New England), WilliamFreeman (Massachusetts Institute of Technology)
Fast Tiered Labeling with Topological PriorsYing Zheng (Duke University - Computer Science), Steve Gu (DukeUniversity - Computer Scie), Carlo Tomasi
Multi-Component Models for Object DetectionChunhui Gu (UC Berkeley), Pablo Arbelaez (UC Berkeley), Yuanqing Lin (NECLaboratories Amertica), Kai Yu (NEC Laboratories Amertica), Jitendra Malik (UCBerkeley)
Joint Classification-Regression Forests for Spatially Structured Multi-Object SegmentationBen Glocker (Microsoft Research Cambridge), Olivier Pauly (TechnischeUniversitaet Muenchen), Ender Konukoglu (Microsoft Research Cambridge), AntonioCriminisi (Microsoft Research Cambridge)
Using linking features in learning non-parametric part modelsLeonid Karlinsky (Weizmann Institute of Science), Shimon Ullman (WeizmannInstitute of Science)
Connecting Missing Links: Object Discovery from Sparse ObservationsHongwen Kang (Carnegie Mellon University), Martial Hebert, Takeo Kanade
Beyond the line of sight: labeling the underlying surfacesRuiqi Guo (UIUC), Derek Hoiem (University of Illinois)
立体视觉与重建
Optimal Templates for Non-Rigid Surface ReconstructionMarkus Moll (K.U.Leuven), Luc Van GoolScale Robust Multi View StereoChristian Bailer, Manuel Finckh (Tuebingen University), Hendrik Lensch (Tuebingen University)
Multiple View Object Cosegmentation using Appearance and Stereo CuesAdarsh Kowdle (Cornell University), Sudipta Sinha, Rick Szeliski
Detection of Independently Moving Objects in Non-planar Scenes via Multi-Frame Monocular Epipolar ConstraintVladimir Reilly (University of Central Florida), Soumyabrata Dey (University of Central Florida), Mubarak Shah (UCF)
3D Reconstruction of Dynamic Scenes with Multiple Handheld CamerasHanqing Jiang (Zhejiang University), Haomin Liu (Zhejiang University), PingTan (National University of Singapore), Guofeng Zhang (Zhejiang University),Hujun Bao (Zhejiang University)
其他
Auto-grouped Sparse Representation for Visual AnalysisJiashi Feng (NUS), Xiaotong Yuan, zilei Wang, Huan Xu, Shuicheng YanUndoing the Damage of Dataset BiasAditya Khosla (MIT), Tinghui Zhou (CMU), Tomasz Malisiewicz (MIT), Alyosha Efros (CMU), Antonio Torralba (MIT)
Unsupervised Discovery of Mid-Level Discriminative PatchesSaurabh Singh (Carnegie Mellon University), Abhinav Gupta, Alyosha Efros (CMU)
A new biologically inspired color- and shape-based image descriptorJun Zhang (Brown University), Youssef Barhomi (Brown University), ThomasSerre (Brown University)
Continuous Regression for Non-Rigid Image AlignmentEnrique Sanchez Lozano (Gradiant), Fernando De la Torre (Carnegie Mellon University), Daniel Gonzalez Jimenez (Gradiant)
Coregistration: Simultaneous Alignment and Modeling of Articulated 3D ShapeDavid Hirshberg (MPI for Intelligent Systems), Matthew Loper (MPI for Intelligent Systems), Eric Rachlin (MPI for Intelligent Systems), MichaelBlack (Max Planck Institute for Intelligent Systems)
Discovering Latent Domains for Multisource Domain AdaptationJudy Hoffman (UC Berkeley), Kate Saenko (UC Berkeley - Harvard - ICSI),Brian Kulis (Ohio State), Trevor Darrell (UC Berkeley - ICSI)
相关文章推荐
- 一些超赞的计算机视觉文献与代码资源
- 一些计算机视觉的代码网址
- 一些超赞的计算机视觉文献与代码资源
- 一些计算机视觉的代码网址
- 计算机视觉一些代码
- 一些超赞的计算机视觉文献与代码资源
- 推荐:计算机视觉的一些新代码下载点
- 一些超赞的计算机视觉文献与代码资源
- 计算机视觉领域的一些牛人博客,超有实力的研究机构等的
- 计算机视觉方向的一些顶级会议和期刊
- [转载]CV 领域 计算机视觉文献与代码资源
- 计算机视觉领域的一些牛人博客,超有实力的研究机构等的网站链接
- 一些计算机视觉杂志的影响因子
- 计算机视觉领域的一些牛人博客,超有实力的研究机构等的网站
- 计算机视觉文献与代码资源及资料
- nature science上关于计算机视觉的一些原创文献
- 计算机视觉方向的一些顶级会议和期刊
- 计算机视觉领域的一些牛人博客,超有实力的研究机构等的网站链接
- 一些关于计算机视觉的资源
- 推荐一些计算机视觉相关的书籍