Statistical and Computational Models in Vision (original) (raw)

This is a graduate level course on computer image analysis. Upon completion, students should be able to sketch a global picture of the computer vision field and have learned fundamental concepts and advanced methods in statistical modeling of images, stochastic computation, and projective geometry.

Instructor: Pedro M. Q. Aguiar, ISR-IST, contact: aguiar@isr.ist.utl.pt

Reading material: There is no textbook for the course. The readings are research papers and selected chapters/sections from various books. The majority of these papers are either linked through the course web page or provided as handouts.

Pre-requisites: Background in computer vision, pattern recognition, statistics. Previous experience on computer vision/image processing projects. MATLAB programming skills.

Grading: Students will be graded according to i) lecture attendance and discussion and ii) a presentation on a specific topic. i) In order to participate discussions effectively, students should read the materials before attending the lectures. ii) Students should identify a topic of their interest as soon as possible and prepare a 60 min. presentation on that topic.

Tentative syllabus:

Introduction. What is vision? Scientific approaches to vision. State of the art. Open problems. Vision as Bayesian inference.

• "A Bayesian formulation of visual perception", D.C. Knill, D. Kersten, and A. Yuille, chapter in "Perception as Bayesian Inference", Cambridge University Press, 1996, (handout). **Image modeling and representation.**Statistical models.
• "What is the goal of sensory coding?", D.J. Field, Neural Computation, 6:559-601, 1994.
• "Sparse coding with an over-complete basis set: A strategy employed by V1?", B.A. Olshausen and D.J. Field, Vision Research, 37:3311-3325, 1997, [.ps].
• "Minimax entropy principle and its applications to texture modeling", S.C. Zhu, Y.N. Wu, and D.B. Mumford, Neural Computation, 1997, [.ps].
• "Edge co-occurence in natural images predicts contour grouping performance", W.S. Geisler, J.S. Perry, B.J. Super, D.P. Gallogly, Vision Research, 41:711-724, 2001.
• "Statistical modeling and conceptualization of visual patterns", S.C. Zhu, Submitted to PAMI, 2002, [.ps]. Stochastic computation. Diffusions, jumps, MCMC.
• "Mean shift, mode Seeking, and clustering", Y. Cheng, PAMI, 17(8):790-799, 1995.
• "Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation", S.C. Zhu and A.L. Yuille, PAMI, 1996, [.ps].
• "Level set methods: an act of violence", J.A. Sethian, A tutorial on level set methods, 1997, [.ps],[web link].
• "Image segmentation by data-driven Markov Chain Monte Carlo", Z.W. Tu and S.C. Zhu, PAMI, 2002, ICCV 2001, [.ps]. Scene geometry. Projective geometry, stereo, multiview geometry, SFM.
• "Shape and motion from image streams: a factorization method", C. Tomasi and T. Kanade, IJCV, 1992, (handout).
• "Self-Calibration and Metric Reconstruction in spite of Varying and Unknown Internal Camera Parameters", Pollefeys, Koch and Gool, IJCV, 32(1), 1999, [pdf].
• "The Manhattan World assumption: regularities in scene statistics which enable Bayesian inference", J. Coughlan and A. Yuille, NIPS 2000, [.ps], ICCV 1999, [.pdf],[web link].
• "Structure from motion without correspondences", F. Dellaert S. M. Seitz C. E. Thorpe S. Thrun, CVPR 2000, [.pdf].
• "The Space of All Stereo Images", S. Seitz, J. Kim, IJCV, 2001, [pdf]. **Motion analysis and tracking.**Condensation, particle filtering.
• "Representing moving images with layers", J. Wang and E.H. Adelson, IEEE Trans. on Image Processing, 3(5):625-638, 1994, (handout).
• "Estimating optical flow in segmented images using variable-order parametric models with local deformations", M.J. Black and A. Jepson, PAMI, 1996, [pdf].
• _"Condensation - conditional density propagation for visual tracking",_M. Isard and A. Blake, IJCV, 1998, [.ps], ECCV 1998,[.ps],[web link]. **Vision with graphics and arts.**Image-based rendering, non-photorealistic rendering.
• "Image-based rendering", D. Forsyth and J. Ponce, chapter in "Computer Vision", [pdf].
• "Plenoptic sampling", J.X. Chai, X. Tong, S.C. Chan, and H. Shum, Siggraph 2000, [pdf].
• "Genealized plenoptic sampling", C. Zhang and T. Chen, CMU TR AMP01-06, 2001, [pdf].
• "Non-Photorealistic Rendering", tutorial at Siggraph 19990, [pdf],[web link].