Statistical and Computational Models in Vision
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.
M. Q. Aguiar, ISR-IST,
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.
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.
Introduction. What is
Scientific approaches to vision. State of the art. Open problems. Vision
as Bayesian inference.
Image modeling and representation.
formulation of visual perception",
D.C. Knill, D. Kersten, and A. Yuille, chapter in "Perception as Bayesian
Inference", Cambridge University Press, 1996, (handout).
Stochastic computation. Diffusions,
"What is the goal of sensory
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,
"Statistical modeling and
conceptualization of visual patterns",
S.C. Zhu, Submitted to PAMI, 2002, [.ps].
Scene geometry. Projective
geometry, stereo, multiview geometry, SFM.
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
J.A. Sethian, A tutorial on level set methods, 1997, [.ps],
"Image segmentation by data-driven
Markov Chain Monte Carlo",
Z.W. Tu and S.C. Zhu, PAMI, 2002, ICCV 2001, [.ps].
Motion analysis and tracking.
Condensation, particle filtering.
"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],
"Structure from motion without
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].
Vision with graphics and arts.
Image-based rendering, non-photorealistic rendering.
"Representing moving images
J. Wang and E.H. Adelson, IEEE Trans. on Image Processing, 3(5):625-638,
"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",
Isard and A. Blake, IJCV, 1998, [.ps],
D. Forsyth and J. Ponce, chapter in "Computer Vision", [pdf].
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].
tutorial at Siggraph 19990, [pdf],