--2009--
1. Image Descriptors
· [SIFT] Lowe, D.G. Distinctive image features from scale-invariant keypoints. IJCV, 2004.·
· [GIST] Oliva, A., Torralba, A. Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV, 2001.
· [Shape Context] Belongie S., Malik J., Puzicha J. Shape Matching and Object Recognition Using Shape Contexts. PAMI, 2002.
· [Geometric Blur] Berg A. C., Malik J. Geometric Blur for Template Matching. CVPR, 2001.·
· [Local Self-Similarity] Shechtman E., Irani M. Matching Local Self-Similarities across Images and Videos. CVPR, 2007.
· [SURF] Bay H., Ess A., Tuytelaars T., Van Gool L. SURF: Speeded Up Robust Features. CVIU, 2008.
· [LBP] Heikkila M., Pietikainen M., Schmid C. Description of interest regions with local binary patterns. Pattern Recognition, 2009.
2. Video Descriptors
[Space-time corners]
· Laptev I. On Space-Time Interest Points. IJCV, 2005.
· Laptev I., Lindeberg T. Space-time Interest Points. ICCV, 2003.
3. Survey / comparison papers for different applications (recognition / matching)
· Zhang J., Marszalek M.,Lazebnik S., Schmid C. Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV, 2007.
· Mikolajczyk K., Schmid C. A performance evaluation of local descriptors. PAMI, 2005.
· Horster E., Greif T., Lienhart R., Slaney M. Comparing local feature descriptors in pLSA-based image models. DAGM, 2008.
4. Efficient search in large image databases
· Torralba A., Fergus R., Freeman W. T. 80 million tiny images: a large dataset for non-parametric object and scene recognition. PAMI, 2008.
· Torralba A., Fergus R, Weiss Y. Small codes and large databases for recognition. CVPR, 2008.
· Weiss Y., Torralba A., Fergus R. Spectral Hashing. NIPS, 2008.
· Nister D, Stewenius H. Scalable recognition with a vocabulary tree. CVPR, 2006.
· Kumar N., Belhumeur P. N., Nayar S. K. FaceTracer: A Search Engine for Large Collections of Images with Faces. ECCV, 2008.
5. Exploiting wealth of huge image libraries
· Hays J., Efros A. Scene Completion Using Millions of Photographs. ACM Transactions on Graphics. SIGGRAPH, 2007.
· Simon, I., Seitz, S. M. Scene Segmentation Using the Wisdom of Crowds. ECCV, 2008.
· Bitouk D., Kumar N., Dhillon S., Belhumeur P. N., Nayar S. K. Face Swapping: Automatically Replacing Faces in Photographs. ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH), 2008.
· Hays J., Efros A. IM2GPS: estimating geographic information from a single image. CVPR, 2008.
· Agarwal S., Snavely N., Simon I., Seitz S.M. and Szeliski R. Building Rome in a Day. ICCV, 2009.
6. Dictionaries for sparse representation modeling
· Aharon M., Elad M., Bruckstein A. M. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process, 2006.
· Bruckstein A. M., Donoho D. L., Elad M. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images. SIAM review, 2009.
· Aharon M., Elad M., Bruckstein A. M. On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them. Linear Algebra and its Applications, 2006.
· Rubinstein R., Bruckstein A. M. Dictionaries for Sparse Representation Modeling. to appear in the IEEE Proceedings – Special Issue on Applications of Compressive Sensing & Sparse Representation.
7. Statistics of natural images
· Zhu S. C., Mumford D. Prior learning and Gibbs reaction-diffusion. PAMI, 1997.
· Roth S., Black M. J.
· Conference version: Fields of experts: A framework for learning image priors. CVPR, 2005.
· Journal version: Fields of experts. IJCV, 2009.
· Weiss Y., Freeman W. T. What makes a good model of natural images?. CVPR, 2007.
8. Blind deconvolution
· Attias H. Independent factor analysis. Neural Computation, 1999.
· Fergus R.,Singh B.,Hertzmann A.,Roweis S.T.,Freeman W.T. Removing camera shake from a single photograph. SIGGRAPH, 2006.
· Joshi N., Szeliski R., Kriegman D. Psf estimation using sharp edge prediction. CVPR, 2008.
· Levin A., Weiss Y., Durand F., Freeman W. Understanding and evaluating blind deconvolution algorithms. CVPR, 2009.
9. Action recognition
Dynamic
· Laptev I., Marszalek M., Schmid C. Learning realistic human actions from movies. CVPR, 2008.
· Gorelick L., Blank M., Shechtman E., Irani M., Basri R. Actions as Space-Time Shapes. PAMI, 2007.
· Laptev I., Pérez P. Retrieving actions in movies. ICCV, 2007.
· Marszalek M., Laptev I., Schmid C. Actions in context. CVPR, 2009.
Static
· Gupta A. , Kembhavi A., Davis L. S. Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition. PAMI, 2009.
· Li-Jia Li, Li Fei-Fei What, where and who? Classifying events by scene and object recognition. ICCV, 2007.
10. Graph cuts
· Kolmogorov, V. and Zabih, R. What Energy Functions can be Minimized via Graph Cuts?. PAMI, 2004.
· Boykov Y., Veksler O., Zabih R. Fast Approximate Energy Minimization via Graph Cuts. ICCV, 1999.
· Kolmogorov V., Rother C. Minimizing non-submodular functions with graph cuts – a review. PAMI, 2007.
· Kohli P., Ladicky L., Torr P. Robust Higher Order Potentials for Enforcing Label Consistency. IJCV, 2009.
· Freedman D., Turek M. Graph cuts with many-pixel interactions: theory and applications to shape modeling. Image and Vision Computing, 2010
--2010--
1. Denoising
· Image denoising using scale mixtures of gaussians in the wavelet domain, J. Portilla, V. Strela, M. Wainwright, E. Simoncelli. In IEEE Trans. Image Processing, 2003.
· A review of image de-noising methods, with a new one., A. Buades, B. Coll, J. Morel. In SIAM Journal on Multiscale Modeling and Simulation, 2005.
· Image denoising by sparse 3-D transform-domain collaborative filtering., K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian. In IEEE Trans. Image Processing, 2007.
· A Tour of Modern Image Processing., P. Milanfar. Invited feature article in review IEEE Signal Processing Magazine, 2010.
· Image Quality Assessment: From Error Visibility to Structural Similarity., W. Zhou, A.C. Bovik, H.R. Sheikh, E.P Simoncelli. In IEEE Trans. Image Processing, 2004.
2. Compressed Sensing
· An Introduction To Compressive Sampling, E.J. Candes, M.B. Wakin. In IEEE Signal Processing Magazine, March 2008.
· Learning compressed sensing, Y. Weiss, H.S. Chang, W.T. Freeman. In Snowbird Learning Workshop, 2007.
· Stable signal recovery from incomplete and inaccurate measurements, E. Candesy, J. Romberg, T. Tao. In Comm. Pure Appl. Math., August 2006.
· Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging, M. Lustig, D. Donoho, J.M. Pauly. In Magnetic Resonance in Medicine, 2007.
· Single-pixel imaging via compressive sampling, M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, R. Baraniuk. In IEEE Signal Processing Magazine, March 2008.
3. Super-Resolution (in images)
· Improving resolution by image registration, M. Irani, S. Peleg. In CVGIP: Graphical Models and Image Processing, 1991.
· Limits on super-resolution and how to break them, S. Baker, T. Kanade. In PAMI, 2002.
· Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation, Z. Lin, H. Shum. In PAMI, 2004.
· Example based super-resolution, W.T. Freeman, T.R. Jones, E.C. Pasztor. In Comp. Graph. Appl., 2002.
· Super-resolution from a single image, D. Glasner, S. Bagon, M. Irani. In ICCV 2009.
4. Shape from Illumination
· Shape from Shading: A Survey, R. Zhang, P. Tsai, J.E. Cryer, M. Shah. In PAMI 1999.
· Optimal Algorithm for Shape from Shading and Path Planning, R. Kimmel, J.A. Sethian. In Journal of Mathematical Imaging and Vision, 2001.
· Efficient Belief Propagation for Vision Using Linear Constraint Nodes, B. Potetz. In CVPR 2007.
· Shape from shading using graph cuts, J.Y. Chang, K.M. Lee, S.U. Lee. In Journal of Pattern Recognition, 2008.
5. Deep Learning
· Learning multiple layers of representation, G.E. Hinton. In TRENDS in Cognitive Sciences, 2007.
· Reducing the dimensionality of data with neural networks, G.E. Hinton, R.R. Salakhutdinov. In Science, July 2006.
· Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, H. Lee, R. Grosse, R. Ranganath, A.Y. Ng. In ICML, 2009.
· What is the Best Multi-Stage Architecture for Object Recognition?, K. Jarrett, K. Kavukcuoglu, M. Ranzato, Y. LeCun. In ICCV, 2009.
· Exploring Strategies for Training Deep Neural Networks, H. Larochelle, Y. Bengio, J. Louradour, P. Lamblin. In Journal of Machine Learning Research, 2009.
6. Random forests
· Semantic Texton Forests for Image Categorization and Segmentation, J. Shotton, M. Johnson, R. Cipolla. In CVPR 2008.
· Object Class Segmentation using Random Forests, F. Schroff, A. Criminisi, A. Zisserman. In BMVC 2008.
· Randomized Trees for Real-Time Keypoint Recognition, V. Lepetit, P. Lagger, P. Fua. In CVPR 2005.
· Regression forests for efficient anatomy detection and localization in CT studies, A. Criminisi, J. Shotton, D. Robertson, E. Konukoglu. In MICCAI MCV 2010.
· Fast discriminative visual codebooks using randomized clustering forests, F. Moosmann, B. Triggs, F. Jurie. In NIPS 2006.
· MIForests: multiple-instance learning with randomized trees, C. Leistner, A. Saffari, H. Bischof. In ECCV 2010.
7. Pascal Grand Challenge
· Image Classification Using Super-Vector Coding of Local Image Descriptors, X. Zhou, K. Yu, T. Zhang, T.S. Huang. In ECCV 2010.
· Object Detection with Discriminatively Trained Part-Based Models, P.F. Felzenszwalb, R.B. Girshick, D. McAllester, D. Ramanan. In PAMI 2010.
· Unbiased Look at Dataset Bias, A. Torralba, A. Efros. In CVPR 2011.
· Multiple Kernels for Object Detection, A. Vedaldi, V. Gulshan, M. Varma, A. Zisserman. In ICCV 2009.
· The PASCAL Visual Object Classes (VOC) Challenge, M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman. In IJCV 2010.
see also:
[1] Interesting Evolutions of Advanced Topics in Computer Vision
[2] Computer Vision Courses from Weizmann Institute of Science
