Topic | Name | Reference | code |
Image Segmentation | Segmentation by Minimum Code Length | AY Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 | |
Image Segmentation | Normalized Cut | J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 | |
Image Segmentation | Entropy Rate Superpixel Segmentation | M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 | |
Image Segmentation | Mean-Shift Image Segmentation - EDISON | D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 | |
Image Segmentation | Efficient Graph-based Image Segmentation - Matlab Wrapper | P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 | |
Image Segmentation | Biased Normalized Cut | S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 | |
Image Segmentation | Multiscale Segmentation Tree | E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 and N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 | |
Image Segmentation | Efficient Graph-based Image Segmentation - C++ code | P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 | |
Image Segmentation | Superpixel by Gerg Mori | X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 | |
Image Segmentation | Segmenting Scenes by Matching Image Composites | B. Russell, AA Efros, J. Sivic, WT Freeman, A. Zisserman, NIPS 2009 | |
Image Segmentation | Recovering Occlusion Boundaries from a Single Image | D. Hoiem, A. Stein, AA Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. | |
Image Segmentation | Quick-Shift | A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 | |
Image Segmentation | SLIC Superpixels | R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 | |
Image Segmentation | Mean-Shift Image Segmentation - Matlab Wrapper | D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 | |
Image Segmentation | OWT-UCM Hierarchical Segmentation | P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 | |
Image Segmentation | Turbepixels | A. Levinshtein, A. Stere, KN Kutulakos, DJ Fleet, SJ Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 | |
Image Super-resolution | MRF for image super-resolution | W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 | |
Image Super-resolution | Single-Image Super-Resolution Matlab Package | R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 | |
Image Super-resolution | Self-Similarities for Single Frame Super-Resolution | C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 | |
Image Super-resolution | MDSP Resolution Enhancement Software | S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 | |
Image Super-resolution | Sprarse coding super-resolution | J. Yang, J. Wright, TS Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 | |
Image Super-resolution | Multi-frame image super-resolution | Pickup, LC Machine Learning in Multi-frame Image Super-resolution, PhD thesis | |
Image Understanding | SuperParsing | J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010 | |
Image Understanding | Discriminative Models for Multi-Class Object Layout | C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 | |
Image Understanding | Nonparametric Scene Parsing via Label Transfer | C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 | |
Image Understanding | Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics | A. Gupta, AA Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 | |
Image Understanding | Towards Total Scene Understanding | L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 | |
Image Understanding | Object Bank | Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 | |
Kernels and Distances | Fast Directional Chamfer Matching | ||
Kernels and Distances | Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1) | H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007 | |
Kernels and Distances | Diffusion-based distance | H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 | |
Low-Rank Modeling | TILT: Transform Invariant Low-rank Textures | Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 | |
Low-Rank Modeling | Low-Rank Matrix Recovery and Completion | ||
Low-Rank Modeling | RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition | Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 | |
MRF Optimization | MRF Minimization Evaluation | R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 | |
MRF Optimization | Max-flow/min-cut for shape fitting | V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 | |
MRF Optimization | Max-flow/min-cut | Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 | |
MRF Optimization | Planar Graph Cut | FR Schmidt, E. Toppe and D. Cremers, Ef?cient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 | |
MRF Optimization | Max-flow/min-cut for massive grids | A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for ND Grids, CVPR 2008 | |
MRF Optimization | Multi-label optimization | Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 | |
Machine Learning | Statistical Pattern Recognition Toolbox | MI Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 | |
Machine Learning | Netlab Neural Network Software | CM Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 | |
Machine Learning | Boosting Resources by Liangliang Cao | http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm | |
Machine Learning | FastICA package for MATLAB | http://research.ics.tkk.fi/ica/book/ | |
Multi-View Stereo | Patch-based Multi-view Stereo Software | Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 |
Topic | Name | Reference | code |
Multi-View Stereo | Clustering Views for Multi-view Stereo | Y. Furukawa, B. Curless, SM Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 | |
Multi-View Stereo | Multi-View Stereo Evaluation | S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 | |
Multiple Instance Learning | DD-SVM | Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004 | |
Multiple Instance Learning | MIForests | C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 | |
Multiple Instance Learning | MILIS | Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010 | |
Multiple Instance Learning | MILES | Y. Chen, J. Bi and JZ Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 | |
Multiple Kernel Learning | SHOGUN | S. Sonnenburg, G. R?tsch, C. Sch?fer, B. Sch?lkopf . Large scale multiple kernel learning. JMLR, 2006 | |
Multiple Kernel Learning | OpenKernel.org | F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 | |
Multiple Kernel Learning | SimpleMKL | A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet.Simplemkl. JMRL, 2008 | |
Multiple Kernel Learning | DOGMA | F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 | |
Multiple View Geometry | MATLAB and Octave Functions for Computer Vision and Image Processing | PD Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns | |
Multiple View Geometry | Matlab Functions for Multiple View Geometry | ||
Nearest Neighbors Matching | ANN: Approximate Nearest Neighbor Searching | ||
Nearest Neighbors Matching | Spectral Hashing | Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 | |
Nearest Neighbors Matching | Coherency Sensitive Hashing | S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 | |
Nearest Neighbors Matching | FLANN: Fast Library for Approximate Nearest Neighbors | ||
Nearest Neighbors Matching | LDAHash: Binary Descriptors for Matching in Large Image Databases | C. Strecha, AM Bronstein, MM Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. | |
Object Detection | Poselet | L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 | |
Object Detection | Cascade Object Detection with Deformable Part Models | P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 | |
Object Detection | Multiple Kernels | A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 | |
Object Detection | Hough Forests for Object Detection | J. Gall and V. Lempitsky, Class-Speci?c Hough Forests for Object Detection, CVPR, 2009 | |
Object Detection | Discriminatively Trained Deformable Part Models | P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 | |
Feature Extraction andObject Detection | Histogram of Oriented Graidents - OLT for windows | N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 | |
Feature Extraction andObject Detection | Histogram of Oriented Graidents - INRIA Object Localization Toolkit | N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 | |
Object Detection | Recognition using regions | C. Gu, JJ Lim, P. Arbelaez, and J. Malik, CVPR 2009 | |
Object Detection | A simple parts and structure object detector | ICCV 2005 short courses on Recognizing and Learning Object Categories | |
Object Detection | Feature Combination | P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 | |
Object Detection | Ensemble of Exemplar-SVMs | T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 | |
Object Detection | A simple object detector with boosting | ICCV 2005 short courses on Recognizing and Learning Object Categories | |
Object Detection | Max-Margin Hough Transform | S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 | |
Object Detection | Implicit Shape Model | B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 | |
Object Detection | Ensemble of Exemplar-SVMs for Object Detection and Beyond | T. Malisiewicz, A. Gupta, AA Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 | |
Object Detection | Viola-Jones Object Detection | P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 | |
Object Discovery | Using Multiple Segmentations to Discover Objects and their Extent in Image Collections | B. Russell, AA Efros, J. Sivic, WT Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 | |
Object Proposal | Objectness measure | B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 | |
Object Proposal | Parametric min-cut | J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 | |
Object Proposal | Region-based Object Proposal | I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 | |
Object Recognition | Recognition by Association via Learning Per-exemplar Distances | T. Malisiewicz, AA Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 | |
Object Recognition | Biologically motivated object recognition | T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 | |
Object Segmentation | Geodesic Star Convexity for Interactive Image Segmentation | V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman.Geodesic star convexity for interactive image segmentation | |
Object Segmentation | ClassCut for Unsupervised Class Segmentation | B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 | |
Object Segmentation | Sparse to Dense Labeling | P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 | |
Optical Flow | Optical Flow by Deqing Sun | D. Sun, S. Roth, MJ Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 | |
Optical Flow | Classical Variational Optical Flow | T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 | |
Optical Flow | Large Displacement Optical Flow | T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 | |
Optical Flow | Dense Point Tracking | N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 | |
Optical Flow | Optical Flow Evaluation | S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 |
待续:计算机视觉与模式识别代码合集第二版three