计算机视觉方面的代码

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~~

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 comparisonSIAM 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 detectorsIJCV, 2005. [PDF]

6.    J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regionsBMVC, 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 DetectionCVPR 2010. [PDF]

10.  N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human DetectionCVPR 2005. [PDF]

11.  A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelopeIJCV, 2001. [PDF]

12.  S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contextsPAMI, 2002. [PDF]

13.  K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene RecognitionPAMI, 2010.

14.  I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF]

15.  J. Kim and K. Grauman, Boundary Preserving Dense Local RegionsCVPR 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 SegmentationPAMI, 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 SegmentationIJCV 2004. [PDF]

4.    D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space AnalysisPAMI 2002. [PDF]

5.    P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image SegmentationPAMI, 2011. [PDF]

6.    A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric FlowsPAMI 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 CompressionCVIU, 2007. [PDF]

10.  S. Maji, N. Vishnoi and J. Malik, Biased Normalized CutCVPR 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 DetectionCVPR 2005. [PDF]

2.    P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
Object Detection with Discriminatively Trained Part Based ModelsPAMI, 2010 [PDF]

3.    P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part ModelsCVPR 2010 [PDF]

4.    L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsICCV 2009 [PDF]

5.    B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and SegmentationIJCV, 2008. [PDF]

6.    P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple FeaturesCVPR 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 analysisPAMI, 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 videosCVPR, 2010. [PDF]

9.    L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statisticsJournal of Vision, 2008. [PDF]

10.  D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered ScenesNIPS, 2004. [PDF]

11.  T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans LookICCV, 2009. [PDF]

12.  M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region DetectionCVPR 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 FeaturesICCV 2005. [PDF]

2.    S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesCVPR 2006[PDF]

3.    J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image ClassificationCVPR, 2010 [PDF]

4.    J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image ClassificationCVPR, 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 DetectionICCV, 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 SegmentationCVPR 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 PhotographsECCV 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 VisionIJCAI 1981. [PDF]

2.    J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]

3.    C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral ThesisMIT 2009. [PDF]

4.    B.K.P. Horn and B.G. Schunck, Determining Optical FlowArtificial 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 principlesCVPR 2010. [PDF]

7.    T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimationPAMI, 2010 [PDF]

8.    T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warpingECCV 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 VisionIJCAI 1981. [PDF]

3.    J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]

4.    B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance LearningPAMI 2011 [PDF]

5.    D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual TrackingIJCV 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 TrackingECCV 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. WeissA Closed Form Solution to Natural Image MattingPAMI 2008 [PDF]

2.    A. Levin, A. Rav-Acha, D. Lischinski. Spectral MattingPAMI 2008. [PDF]

3.    Y. Zheng and C. Kambhamettu, Learning Based Digital MattingICCV 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 AssessmentTIP 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 ModelTIP 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 algorithmsIJCV 2002 [PDF]

Structure from motion

·         Boundler [1] [Project]

 

1.    N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3DSIGGRAPH, 2006. [PDF]

Distance Transformation

·         Distance Transforms of Sampled Functions [1] [Project]

1.    P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functionsTechnical 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 MatchingCVPR 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 learningJMLR, 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 learningCVPR, 2010. [PDF]

4.    A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimplemklJMRL, 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 TreesECCV 2010. [PDF]

2.    Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selectionPAMI 2010. [PDF]

3.    Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance SelectionPAMI 2006 [PDF]

4.    Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with RegionsJMLR 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]

 

 

Useful Links(dataset, lectures, and other softwares)

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

 

图像处理:
全局特征
局部特征
图像质量评价
显著性检测
图像滤波

IP: Image Process
Global Feature
Local Feature
Image Quality Analysis
Salience Detection 
Image Filtering

Year

Topic

Method

Reference (Formal)

2009

Global Feature

PHOG: Pyramids of Histograms of Oriented Gradients

A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007

2009

Global Feature

Gist

A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001

2009

Local Feature

SIFT: Scale Invariant Feature Transform

D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.

2010

Local Feature

Affine-SIFT: Affine-Scale Invariant Feature Transform

J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009

2011

Local Feature

LBP: Local Binary Pattern

M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial

2012

Local Feature

PCA-SIFT: Principal Component Analysis - Scale Invariant Feature Transform

Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004

2012

Local Feature

SC: Shape Context

S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002

2012

Image Quality Analysis

SSim: Structure Similarity

Image quality assessment: from error visibility to structural similarity [J]. IEEE Trans. Image Process, 2004, 13(4): 600–612.

2012

Image Quality Analysis

IW-SSim: Information Content Weighted Structure Similarity

Z. Wang and Q. Li, "Information content weighting for perceptual image quality assessment," IEEE Transactions on Image Processing, vol. 20, no 5, pp. 1185-1198, May 2011.

2012

Image Quality Analysis

MS-SSim: Multi-scale Structure Similarity

Wang Z, Simoncelli E P, Bovik A C. Multi-scale  structural similarity for image quality assessment [J].  Proc. IEEE Asilomar Conf. Signals, Syst.Comput., 2003:. 1398–1402.

2012

Image Quality Analysis

MSEMean Square Error

Wang Z,  Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Trans. Image Process, 2004, 13(4): 600–612.

2012

Image Quality Analysis

VSNR: Visual Signal-to-Noise Ratio

Chandler D M, Hemami S S. VSNR: a Wavelet based visual signal-to-noise ratio for  natural images [J]. IEEE Trans. Image Process, 2007,16(9): 2284–2298.

2012

Image Quality Analysis

3-SSIM: 3 -Chanle Structure Similarity

Li C and  Bovik A C. Three-component weighted structural similarity index[C]\\ Proceedings of the International Society for Optical Engineering, 2009.

2012

Image Resizing

Context-Aware:Context

Shai Avidan, Ariel Shamir. Seam carving for content-aware image resizing. ACM SIGGRAPH '07. 26(3). 2007

2012

Salience Detection

Itti Model

Itti, L. A model of saliency-based visual attention for rapid scene analysis . Pattern Analysis and Machine Intelligence, IEEE Transactions on. 20(11): 1254 - 1259. 1998.

2012

Salience Detection

MSSS: Saliency Detection using Maximum Symmetric

Achanta, R.; Süsstrunk, S. Saliency detection using maximum symmetric surround. Image Processing (ICIP), 2010 17th IEEE International Conference on. 2653 - 2656, 2010.

2012

Salience Detection

AIM: Attention based on Information Maximization

Bruce, N.D.B., Tsotsos, J.K., Saliency Based on Information Maximization. Advances in Neural Information Processing Systems, 18, pp. 155-162, June 2006. Selected for oral presentation

2012

Salience Detection

SF: Saliency Filters: Contrast Based Filtering for Salient Region Detection

Perazzi, F. Krahenbuhl, P. ; Pritch, Y. ; Hornung, A. Saliency Filters: Contrast Based Filtering for Salient Region Detection. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. 733 - 740. 2012

2012

Salience Detection

SR: Sspectral Residual

Xiaodi Hou; Liqing Zhang. Saliency Detection: A Spectral Residual Approach. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on. 1-8. 2007.

2012

Salience Detection

HC: Histogram-based Contrast,  RC: Region-based Contrast

M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011

2012

Salience Detection

CRF: Conditional Random Field

Tie Liu; Jian Sun; Nan-Ning Zheng; Xiaoou Tang; Heung-Yeung Shum. Learning to Detect A Salient Object. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on. 1-8. 2007.

2012

Salience Detection

IG: Interest Gaussian

R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009

2012

Salience Detection

Context-Aware:Context

S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010.

2012

Salience Detection

Salient region detection and segmentation.

R. Achanta, F. Estrada, P. Wils, and S. S¨usstrunk. Salient region detection and segmentation. In ICVS, pages 66–75. Springer, 2008. 410, 412, 414

2012

Salience Detection

GBVSGraph-Based Visual Saliency

J. Harel, C. Koch, and P. Perona. Graph-based visua saliency. In NIPS, pages 545–552, 2006. 410, 412, 414

2012

Salience Detection

SUNSaliency Using Natural statistics

A Bayesian Framework for Saliency Using Natural Statistics

2012

Salience Detection

Fuzzy Growing

Y.-F. Ma and H.-J. Zhang, “Contrast-based image attention analysis by using fuzzy growing,” ACM International Conference on Multimedia, pp. 374–381, November 2003.

2012

Salience Detection

DSDDiscriminant Saliency Detector

Achanta, R. Discriminant Saliency for Visual Recognition from Cluttered Scenes[C]/Proc. Of IEEE  Conference Publications. On Hong Kong IEEE press. 2010,Pages: 2653 - 2656

2012

Salience Detection

HSHuman Saliency

Judd, T. Ehinger, K. Learning to Predict Where Humans Look[C]/Proc. Of IEEE  Conference Publications. On Kyoto ,Pages:2106 - 2113

2009

Image Filtering

BF: Bilateral Filtering

S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006

2012

Image Filtering

BF: Bilateral Filtering

Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering, CVPR 2009

2012

Image Filtering

BF: Bilateral Filtering

Q. Yang, S. Wang and N. Ahuja , Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010

2009

Image Filtering

BF: Bilateral Filtering

S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006







 



机器学习

判决模型
生成模型
图模型
聚类
流形
核方法
距离函数
迁移学习
集成学习


ML: Machine Learning

Discriminative Model
Generated Model
Graph Model
Clustering
Manifold
Kernel
Distance
Transfer Learning
Ensemble Learning

2008

Discriminative Model

SVM: Support Vector Machines

C.-W. Hsu, C.-J. Lin. A simple decomposition method for support vector machines , Machine Learning 46(2002), 291-314

2010

Discriminative Model

LDA: Linear Discriminant Analysis

C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011.

2012

Discriminative Model

Netlab: Networks Laboratory

C. M. Bishop, Neural Networks for Pattern RecognitionOxfordUniversity Press, 1995

2009

Generated Model

PLSA: Probabilistic Latent Semantic Analysis

Fei-Fei, L. and Perona, P., "A Bayesian Heirarcical Model for Learning Natural Scene Categories", Proc. CVPR, 2005.

2010

Generated Model

LDA: Latent Dirichlet Allocation

Tracking E. B. Graphical Models for Visual Object Recognition and Sudderth Doctoral Thesis, Massachusetts Institute of Technology, May 2006.

2010

Generated Model

HDP: Hierarchical Dirichlet Processes

Targets E. Fox, E. Sudderth, and A. Willsky. Hierarchical DirichletProcesses for Tracking Maneuvering International Conference on Information Fusion, July 2007.

2010

Generated Model

TDP: Transformed Dirichlet Processes

Processes E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Describing Visual Scenes using Transformed Dirichlet. Neural Information Processing Systems, Dec. 2005.

2009

Graph Model

CRF: Conditional Random Field, MRF: Markov Random Field

S. V. N. Vishwanathan. Nicol N. Schraudolph. Mark W. Schmidt. Kevin P. Murphy. Accelerated training of conditional random fields with stochastic gradient methods. Proceeding ICML '06 Proceedings of the 23rd international conference on Machine learning. Pages 969 - 976. 2006.

2009

Graph Model

ICM: Iterated Conditional Modes

S Li. Markov Random Field Modeling in Computer Vision Springer-Verlag, 1995

2010

Clustering

AP: Affinity Propagation (k-centers; k-means; klogk; mdgEM: Mixture Directional Gaussian - Exception Maximum; migEM: Mixture Isotropic Gaussian - Exception Maximum;Clusteing with Quantized/ Quantized Extension)

Clustering by Passing Messages Between Data Points. Brendan J. Frey and Delbert Dueck, Science 315, 972–976, February 2007.

2010

Manifold

PCA: Principal Component Analysis, LE: Laplacian Eigenmap, LLE: Local Linear Embedding, HLLE: Hessian Local Linear Embedding, Isomap: Isometric Feature Mapping

L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik.Dimensionality Reduction: A Comparative Review. Tilburg UniversityTechnical Report, TiCC-TR 2009-005, 2009.

2012

Kernel

SKMsmo: Support Kernel Machine - Sequential Minimal Optimization

Bach, F.R. Lanckriet, G.R.G., Jordan , M.I. Fast Kernel Learning using Sequential Minimal Optimization . Technical Report CSD-04-1307, Division of Computer Science, University of California , Berkeley . 2004

2012

Kernel

SimpleMKL: Simple Multi-Kernel Learning

A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008

2012

Distance

EMD: Earth Mover's Distance

H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007

2012

Distance

Pwmetric: Pair-Wise Metric

Modeling and Estimating Persistent Motion with Geometric Flows. DahuaLin, Eric Grimson, and John Fisher. 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

2009

Ensemble Learning

Boosting

A. Vezhnevets, O. Barinova . Avoiding Boosting Overfitting by Removing 'Confusing Samples. ECML 2007, Oral.

2009

Ensemble Learning

Boosting

Theoretical and Empirical Analysis of Diversity in Non-Stationary Learning, R. Stapenhurst and G. Brown, 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. 2011.

2009

Ensemble Learning

Alignment

Z. H. Zhou, W. Tang. Clusterer Ensemble [J]. Knowledge-Based Systems, 2006, 19(1): 77-83

2012

Transfer Learning

CCTL: Cross Category Transfer Learning

Guo-Jun Qi, Charu Aggarwal, Yong Rui, Qi Tian, Shiyu Chang and Thomas Huang. Towards Cross-Category Knowledge Propagation for Learning Visual Concepts, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, Colorado, June 21-23, 2011.

2012

Transfer Learning

MSTR: Multi-Source Transfer Learning

Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He. Transfer learning from multiple source domains via consensus regularization. Proceeding CIKM '08 Proceedings of the 17th ACM conference on Information and knowledge management. Pages 103-112. 2008.




计算机视觉: 

图像超分辨率重建
图像配准
图像分割
图像抠图
图像修补
图像分类
图像检索
图像理解
光流
目标跟踪
图像深度估计
语义分析
数据集


CV: Computer Vision
Image Super-Resolution
Image Registration
Image Segmentation
Image Matting
Image Inpainting
Image Classification
Image Retrieval
Image Understanding
Optical Flow
Object Tracking
Image Depth
Semantic Analysis
Data Set

2012

Image Super-Resolution

Super-resolution as Sparse Representation

Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image Super-resolution as Sparse Representation of Raw Image Patches. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

2009

Image Registration

Base on SIFT(Scale Invariant Feature Transform)

D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.

2011

Image Segmentation

SP: Super Pixcels

X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003

2012

Image Segmentation

GC: Graph Cut (Max Flow/ Min Cut)

L. Gorelick, A. Delong, O. Veksler, Y. Boykov, Recursive MDL via Graph Cuts: Application to Segmentation, International Conference on Computer Vision. 2011,

2012

Image Segmentation

Ncut: Normal Cut

J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000

2012

Image Matting

Closed-Form Solution

AnatLevin,DaniLischinski,andYairWeiss.A Closed-Form Solution to Natural Imae Matting,2006

2012

Image Matting

SpectralMatting

AnatLevin,AlexRav-Acha,andDaniLischinski. Spectral Matting,2008

2012

Image Matting

KnockOut

A. Berman, A. Dadourian, and P. Vlahos. Method for removing from an image the background surrounding a selected object,2000

2012

Image Matting

BayesianMatting

Yung-Yu Chuang,Brian Curless1David H. Salesin1, Richard Szeliski.A Bayesian Approach to Digital Matting,2000

2012

Image Matting

Learning Based Matting

YuanjieZheng,ChandraKambhamettu.Learning Based Digital Matting,2009

2012

ImageInpainting

Criminisi Inpainting

Antonio Criminisi, Patrick Perez, and KentaroToyama.Object Removal by Exemplar-Based Inpainting,2003

2012

image Classification

SC: Sparse Coding

Sparse Coding for Image Classification

2010

image Classification

ICA : Independent Component Analysis

Hyvärinen A. Testing the ICA mixing matrix based on inter-subject or inter-session consistency.NeuroImage.

2010

image Classification

FastICA: Fast Independent Component Analysis

A. Hyvärinen, J. Karhunen, E. Oja . Independent Component Analysis. Wiley-Interscience. 2001

2010

Image Classification

SPM: Spatial Pyramid Matching, BoF: Bag of Feature (BoW: Bag of Word)

S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York , June 2006, vol. II, pp. 2169-2178.

2011

Image Classification

LLC: Locality-constrained Linear Coding

Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR'09.

2011

Image Classification

EMK: Efficient Match Kernels

Liefeng Bo, Cristian Sminchisescu Efficient Match Kernels between Sets of Features for Visual Recognition, Advances in Neural Information Processing Systems (NIPS), December, 2009.

2008

Image Retrieval

The Pyramid Match: Efficient Matching for Retrieval and Recognition

K. Grauman and T. Darrell.  The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005

2012

Image Understanding

TSU: Towards Total Scene Understanding

Li-Jia Li, Richard Socher and Li Fei-Fei. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework. Computer Vision and Pattern Recognition (CVPR) 2009.

2012

Image Understanding

Object Context

Yong Jae Lee and Kristen Grauman. Object-Graphs for Context-Aware Category Discovery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco , CA , June 2010.

2012

Optical Flow

Black and Anandan's Optical Flow

Black, M.J. Anandan, P. A framework for the robust estimation of optical flow. Computer Vision, 1993. Proceedings. Fourth International Conference on. 1993.

2012

Object Tracking

PF: Particle Filter (LASSO: Least Absolute Shrinkage and Selection Operator)

X. Mei and H. Ling. Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 33(11):2259--2272, 2011.

2012

Object Tracking

Incremental Learning

D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007

2012

Object Tracking

On-Line Boosting

Tracking the Invisible: Learning Where the Object Might be H. Grabner, J. Matas, L. Van Gool, and P.Cattin In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

2012

Object Tracking

Motion Tracking

C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000

2012

Object Tracking

Kanade-Lucas-Tomasi Feature Tracker

B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981

2012

Object Tracking

Tracking Decomposition

J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010

2012

Object Tracking

Adaptive Structural Local Sparse Appearance Model

Xu Jia, Huchuan Lu, Minghsuan Yang, Visual Tracking via Adaptive Structural Local Sparse Appearance Model, International Conference on Computer Vision and Pattern Recognition,2012,.

2012

Object Tracking

Sparsity-based Collaborative Model

Wei Zhong, Huchuan Lu, Minghsuan Yang, Robust Object Tracking via Sparsity-based Collaborative Model, International Conference on Computer Vision and Pattern Recognition,2012.

2012

Image Depth

DC: Dark Channel

Kaiming He, Jian Sun, and Xiaoou Tang, Single Image Haze Removal using Dark Channel Prior, by  in TPAMI 2011.

2010

Semantic Analysis

Wordnet

WordNet 3.0 Reference Manual

2008

Data Set

Caltech 256: Caltech-256 benchmarks

Citation: caltech-256 object Gategory dataset[c].Greg GriffinAlex Holub,California Institute of Technology on 2007

2008

Data Set

VOCdevkit: PASCAL VOC Development Kits (PASCAL: Pattern Analysis, Statistical Modelling and Computational Learning)

Citation: The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Development Kit.Mark EveringhamJohn WinnMark Everingham John Winn


2009

Data Set

LabelMe

Citation: Modeling the shape of the scene: a holistic representation of the spatial envelope. A. Oliva, A.Torralba. International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.


2009

Data Set

Eight outdoor scene categories

Aude Oliva, Antonio Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.


2009

Data Set

Fifteen Scene Categories

Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006.


2009

Data Set

SUN Database: Scene UNderstanding Database.

J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A.Torralba. SUN Database: Large scale Scene Recognition from Abbey to Zoo. IEEE Conference on Computer Vision and Pattern Recognition. CVPR. 2010.


2012

Data Set

SegBanch: The Berkely Segmentation Dataset and Benchmark

VOI


2012

Data Set

Saliency Benchmark

R. Subramanian, H. Katti, N. Sebe1, M. Kankanhalli, T-S. Chua, An Eye Fixation Database for Saliency Detection in Images,  European Conference on Computer Vision (ECCV 2010), Heraklion, Greece, September 2010


2012

Data Set

SegBanch: The Berkely Segmentation Dataset and Benchmark

X. Ren, C. Fowlkes, J. Malik. "Figure/Ground Assignment in Natural Images", ECCV, Graz , Austria, (May 2006).


2012

Data Set

Flikcer

Citation: Flickr shapetiles : Location data created fromWOEid geotagged Flickr photos


2012

Data Set

YL face: Yale Face Database

Citation: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting andPose[J].Georghiades, A.S. and Belhumeur .IEEE Trans. Pattern Anal. Mach. Intelligence on 2001.pages:643-660


2012

Data Set

Saliency Benchmark

R. Subramanian, H. Katti, N. Sebe1, M. Kankanhalli, T-S. Chua, An Eye Fixation Database for Saliency Detection in Images,  European Conference on Computer Vision (ECCV 2010), Heraklion, Greece, September 2010


2012

Data Set

ImageCLEF Plant (CLEF: key/ french)

Goëau, Hervé; Bonnet, Pierre; Joly, Alexis; Boujemaa,Nozha; Barthelemy, Daniel; Molino, Jean-François;Birnbaum, Philippe; Mouysset, Elise; Picard, Marie. The CLEF 2011 plant image classification task. CLEF 2011 working notes, Amsterdam , The Netherlands, 2011.


2012

Data Set

ImageCLEFphoto (CLEF: key/ french)

Citation: Diversity in Photo Retrieval: Overview of theImageCLEFPhoto Task 2009. Monica Lestari Paramita, Mark Sanderson,Lecture Notes in Computer Science, 2010, Volume 6242/2010, 45-59,


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。

显著性相关

Depth Matters: Influence of Depth Cues on Visual Saliency
Lang 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 Priors
Yichen Wei (Microsoft Research), Fang Wen, Wangjiang Zhu (Tsinghua University), Jian Sun (Microsoft Research Asia)
Saliency Modeling from Image Histograms
Shijian Lu (I2R - A*STAR), Joo-Hwee Lim (Institute for Infocomm Research)
Salient Object Detection: A Benchmark
Ali 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 Tracking
Bo 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 Tracking
Stefan 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 Scenes
Xuemei Zhao (Univ. of Southern California), Dian Gong (Univ. of Southern California), Gerard Medioni (University of Southern California)
Divergence-free motion estimation
Dominque BÈrÈziat (UPMC), Isabelle Herlin (INRIA), Nicolas Mercier (INRIA),Sergiy Zhuk (CWI)
Coherent Filtering: Detecting Coherent Motions from Clutters
Bolei Zhou (The Chinese University of HK), Xiaogang Wang (The ChineseUniversity of HK), Xiaoou Tang
Statistical Inference of Motion in the Invisible
Haroon Idrees (UCF), Imran Saleemi (UCF), Mubarak Shah (UCF)
Group Tracking: Exploring Mutual Relations for Multiple Object Tracking
Genquan Duan (Tsinghua University), Song Cao (Tsinghua University),Haizhou Ai (Tsinghua University), Shihong Lao (Omron Company)
Stixels motion estimation without optical flow computation
Bertan G¸nyel (KU Leuven), Rodrigo Benenson (KU Leuven), Radu Timofte (KULeuven), Luc Van Gool (KU Leuven)
Simultaneous Compaction and Factorization of Sparse Image Motion Matrices
Susanna Ricco (Duke University), Carlo Tomasi
Efficient Nonlocal Regularization for Optical Flow
Philipp Krähenb¸hl (Stanford University), Vladlen Koltun (Stanford University)
Scale Invariant Optical Flow
Li Xu (CUHK), Zhenlong Dai (CUHK), jiaya Jia (CUHK)
A Naturalistic Open Source Movie for Optical Flow Evaluation
Daniel 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 Occlusions
Fei 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 Laplacain
Inchang Choi (KAIST), Yu-Wing Tai (KAIST), Minhaeng Lee (KAIST)
Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing Data
Quanyi Mo (Colorado State University), Bruce Draper (Colorado StateUniversity)
Multi-Scale Clustering of Frame-to-Frame Correspondences for Motion Segmentation
Ralf Dragon (Leibniz Universit\auml),t Hannover, Bodo Rosenhahn, Joern Ostermann
Learning to segment a video to clips based on scene and camera motion
Adarsh Kowdle (Cornell University), Tsuhan Chen (Cornell University)
Efficient Articulated Trajectory Reconstruction using Dynamic Programming and Filters
Jack 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 Camera
Miguel 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 Videos
Sudheendra Vijayanarasimhan, Kristen Grauman
Streaming Hierarchical Video Segmentation
Chenliang 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 ActivityPrediction
Kang 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 ActionRecognition
Sameh Khamis (University of Maryland), Vlad Morariu (University of Maryland), Larry Davis (University of Maryland)
Script Data for Attribute-based Recognition of Composite Activities
Marcus 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 ActivityRecognition
Wongun Choi (The University of Michigan), Silvio Savarese (The University of Michigan - Ann Arbor)
Activity Forecasting
Kris Kitani (Carnegie Mellon University), James Bagnell, Martial Hebert
Propagative Hough Voting for Human Activity Recognition
Gang YU (NTU), Junsong Yuan (NTU), Zicheng Liu (MSR)
Human Actions as Stochastic Kronecker Graphs
Sinisa Todorovic (Oregon State University)
Trajectory-Based Modeling of Human Actions with Motion Reference Points
Yu-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 Sports
Cem Direkoglu (Dublin City University), Noel O’Connor (Dublin City University)
Real–Time Human Pose Tracking using Range Cameras
Varun Ganapathi (Google), Christian Plagemann (Google Research), DaphneKoller (Stanford University), Sebastian Thrun (Google)

目标检测与分割

Object Co-detection
Yinzge 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 Segmentation
Frank Schmidt (ESIEE), Yuri Boykov (University of Western Ontario)
Background Subtraction using Group Sparsity and Low Rank constraint
Xinyi Cui (Rutgers University), Junzhou Huang, shaoting Zhang (Rutgers University), Dimitris Metaxas (Rutgers University)
Shape Sharing for Object Segmentation
Jaechul Kim (University of Texas at Austin), Kristen Grauman
On Learning Higher-Order Consistency Potentials for Multi-class Pixel Labeling
Kyoungup Park (ANU), Stephen Gould (ANU)
Object detection using strongly-supervised deformable part models
Hossein Azizpour (KTH), Ivan Laptev
Hough Regions for Joining Instance Localization and Segmentation
Hayko 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 Detection
Nima Razavi (ETH Zurich), Juergen Gall (ETH Zurich), Pushmeet Kohli, LucVan Gool
Annotation Propagation in Large Image Databases via Dense Image Correspondence
Michael Rubinstein (MIT), Ce Liu (Microsoft Research New England), WilliamFreeman (Massachusetts Institute of Technology)
Fast Tiered Labeling with Topological Priors
Ying Zheng (Duke University - Computer Science), Steve Gu (DukeUniversity - Computer Scie), Carlo Tomasi
Multi-Component Models for Object Detection
Chunhui 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 Segmentation
Ben 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 models
Leonid Karlinsky (Weizmann Institute of Science), Shimon Ullman (WeizmannInstitute of Science)
Connecting Missing Links: Object Discovery from Sparse Observations
Hongwen Kang (Carnegie Mellon University), Martial Hebert, Takeo Kanade
Beyond the line of sight: labeling the underlying surfaces
Ruiqi Guo (UIUC), Derek Hoiem (University of Illinois)

立体视觉与重建

Optimal Templates for Non-Rigid Surface Reconstruction
Markus Moll (K.U.Leuven), Luc Van Gool
Scale Robust Multi View Stereo
Christian Bailer, Manuel Finckh (Tuebingen University), Hendrik Lensch (Tuebingen University)
Multiple View Object Cosegmentation using Appearance and Stereo Cues
Adarsh Kowdle (Cornell University), Sudipta Sinha, Rick Szeliski
Detection of Independently Moving Objects in Non-planar Scenes via Multi-Frame Monocular Epipolar Constraint
Vladimir Reilly (University of Central Florida), Soumyabrata Dey (University of Central Florida), Mubarak Shah (UCF)
3D Reconstruction of Dynamic Scenes with Multiple Handheld Cameras
Hanqing 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 Analysis
Jiashi Feng (NUS), Xiaotong Yuan, zilei Wang, Huan Xu, Shuicheng Yan
Undoing the Damage of Dataset Bias
Aditya Khosla (MIT), Tinghui Zhou (CMU), Tomasz Malisiewicz (MIT), Alyosha Efros (CMU), Antonio Torralba (MIT)
Unsupervised Discovery of Mid-Level Discriminative Patches
Saurabh Singh (Carnegie Mellon University), Abhinav Gupta, Alyosha Efros (CMU)
A new biologically inspired color- and shape-based image descriptor
Jun Zhang (Brown University), Youssef Barhomi (Brown University), ThomasSerre (Brown University)
Continuous Regression for Non-Rigid Image Alignment
Enrique Sanchez Lozano (Gradiant), Fernando De la Torre (Carnegie Mellon University), Daniel Gonzalez Jimenez (Gradiant)
Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape
David 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 Adaptation
Judy Hoffman (UC Berkeley), Kate Saenko (UC Berkeley - Harvard - ICSI),Brian Kulis (Ohio State), Trevor Darrell (UC Berkeley - ICSI)

你可能感兴趣的:(计算机视觉方面的代码)