Selected Topics in Computer Vision

Dr Vincent Lepetit - Dr Fethallah Benmansour - Dr Kevin Smith - Dr Yunpeng Li - Dr Mario Christoudias

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Hints

A good way to start your presentation is by showing results: This way, your audience will understand quickly what is the problem the paper tackles, and what kind of results they can expect.
Use formulas only when necessary, fewer is better.
You should have about 1 slide / minute;
Your presentation should last 20 minutes. Allow 2 or 3 minutes for questions.

Image Denoising

D1: C. Liu and W. T. Freeman. A High-Quality Video Denoising Algorithm based on Reliable Motion Estimation. ECCV 2010.
D2: J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Non-Local Sparse Models for Image Restoration. ICCV 2009.
D3: S. Roth and M.J. Black. Fields of experts: A framework for learning image priors. CVPR 2005.
D4: M. Elad and M. Aharon. Image denoising via learned dictionaries and sparse representation. CVPR 2006.
D5: R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, W. T. Freeman. Removing camera shake from a single photograph. Siggraph 2006.

Image Segmentation

S1: Vikram V. Appia and Anthony J. Yezzi. Fully Isotropic Fast Marching Methods on Cartesian Grids. ECCV 2010.
S2: Gabriel Peyre, Jalal Fadili, and Julien Rabin. Wasserstein Active Contours. SSVM 2011
S3: R. Kimmel and A.M. Bruckstein. On regularized Laplacian zero crossings and other optimal edge integrators. IJCV 2003.
S4: A. Tsai, A. J. Yezzi, and A. S. Willsky. A Curve Evolution Approach to Smoothing and Segmentation Using the Mumford-Shah Functional. CVPR 2000.
S5: C. Xu and J.L. Prince. Snakes, Shapes, and Gradient Vector Flow. TIP 1998.

2D Tracking

T1: Robust Tracking-by-Detection using a Detector Confidence Particle Filter, by Breitenstein, Reichlin, Leibe, Koller-Meier, and Van Gool. ICCV 2009.
T2: BRAMBLE: A Bayesian Multi-Blob Tracker, by Isard and MacCormick. ICCV 2001.
T3: MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets, by Khan, Balch, and Dellaert. PAMI 2005.
T4: Coupled Detection and Trajectory Estimation for Multi-Object Tracking, by Leibe, Schindler, and Van Gool. ICCV 2007.
T5: Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene by Li, Huang, and Nevatia. CVPR 2009.
T6: Pedestrian Tracking by Associating Tracklets using Detection Residuals, by Singh, Wu, and Nevatia. WMVC 2008.

Graphical Models

G1: Efficient Belief Propagation for Early Vision, Pedro F. Feltzenszwalb and Daniel P. Huttenlocher. IJCV 2006.
G2: Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference, Tamir Hazan and Amnon Shashua. UAI 2008.
G3: What Energy Functions Can Be Minimized via Graph Cuts?, Vladimir Kolmogorov and Ramin Zabih. PAMI 2004.
G4: Exact optimization for Markov random fields with convex priors, Hiroshi Ishikawa. PAMI 2003.
G5: Higher-Order Clique Reduction in Binary Graph Cut, Hiroshi Ishikawa. CVPR 2009.

Data Clustering

C1: Carson, Belongie, Greenspan and Malik. Blobworld: Image Segmentation Using Expectation-Maximization and its Application to Image Querying. PAMI 2002.
C2: D. Comaniciu and P. Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002.
C3: Yizong Cheng. Mean shift, mode seeking, and clustering. PAMI 1998.