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ICCV 2005: Papers I Liked

ICCV 2005: Papers I Liked. Vaibhav Vaish. Beijing, China October 15-21. Conference Statistics. 244 papers 45 oral presentations 199 posters ≈ 1670 abstracts registered 11 workshops 7 short courses Computer Vision Contest. Papers I Liked: A Categorisation. Down with parameters!

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ICCV 2005: Papers I Liked

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  1. ICCV 2005: Papers I Liked Vaibhav Vaish Beijing, China October 15-21

  2. Conference Statistics • 244 papers • 45 oral presentations • 199 posters • ≈ 1670 abstracts registered • 11 workshops • 7 short courses • Computer Vision Contest

  3. Papers I Liked: A Categorisation • Down with parameters! • When light behaves badly • Vision discovers Stephen Boyd • SIGGRAPH Techniques • Multi-dimensional Miscellany

  4. ? ? ? Contest: Where am I ? • Given some images of a city taken from known locations, estimate locations of other images • Contest web page with data sets, results, other contest ideas proposed • Rick Szeliski’s PPT (overview)

  5. Non-parametric Methods • Space-time Scene Manifolds • Y. Wexler, D. Simakov • Detecting Irregularities in Images and Video • O. Boiman, M. Irani Space-time Video Completion, Wexler et al, CVPR 2004

  6. Space-time Scene Manifolds Input Video

  7. Space-time Scene Manifolds

  8. Space-time Scene Manifolds Input Video

  9. Space-time Scene Manifolds

  10. Detecting Irregularities in Images and Video How well can we compose an image region with patches from observed image(s) ?

  11. Detecting “unseen” poses Input database

  12. Detecting “unseen” behavior

  13. Detecting Saliency in Video

  14. Detecting Saliency in a Single Image

  15. Discussion • Unsupervised learning method • Training set easy to acquire • Learn large class of poses / behaviors from small set of images • Technical Contributions • Patch descriptors • Inference algorithm • Potential applications • Detecting click fraud in click stream ? • Detecting suspicious behavior in network traffic ? • Finding unusual cells in microscope images ? Honorable Mention, Marr Prize

  16. When Light Behaves Badly • Structured Light in Scattering Media • S. Narasimhan, S. Nayar, B. Sun, S. Koppal • Dynamic Refraction Stereo • N. Morris, K. Kutulakos • A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation • K. Kutulakos, E. Steger • A Theory of Inverse Light Transport • S. Seitz, Y. Matsushita, K. Kutulakos

  17. Structured Light in Scattering Media Stripe-based Range Scanning • Detect stripe in presence of scattering • Remove effect of scatter on appearance

  18. Structured Light in Scattering Media Photometric Stereo • Requires minimum 5 views • Recovers normals and depth Video

  19. <N,K,M> Triangulation Refractive and Specular 3D Shape Given: • N calibrated cameras • K light bounces (reflection or refraction) • M 3D reference points for every pixel’s light path When can we reconstruct depth per pixel ?

  20. Refractive and Specular 3D Shape <1 camera, 1 bounce, 2 reference points> is solvable (simply by ray intersection)

  21. Refractive and Specular 3D Shape • <3 cameras, 2 bounces, 2 ref. points> is solvable given refractive index / mirror knowledge • <N cameras, 3 bounces, 2 ref. points> is not solvable

  22. The Complete Analysis

  23. Experiment: <5,2,2> Triangulation

  24. Experiment: <5,2,2> Triangulation Honorable Mention, Marr Prize

  25. EE 364 @ ICCV: Global Minima • Multiple View Geometry and the L∞ norm • F. Kahl • Quasiconvex Optimization for Robust Geometric Reconstruction • Q. Ke, T. Kanade • Globally Optimal Estimates for Geometric Reconstruction Problems • F. Kahl, D. Henrion • Globally Optimal Solutions for Energy Minimization in Stereo Vision using Reweighted Belief Propagation • T. Meltzer, C. Yanover, Y. Weiss

  26. Seeking the Global Minimum • Reconstruction problems in vision: • Epipolar geometry from a stereo pair • Homography between 2 planes • Multi-view triangulation (3D reconstruction) • Camera calibration • Current Approaches: • Find correspondences • Initial estimate (SVD, linear optimization) • Bundle adjustment (gradient descent for local minima)

  27. Seeking the Global Minimum • [Ke & Kanade, Kahl] • Minimize the largest error (L∞ norm) • Quasiconvex problem (SOCP) • Variant: minimize mth largest error [Ke & Kanade] to tolerate m-1 outliers • [Kahl & Henrion] • Finds global minimum for L2 norm • Progressive approximation by SDP • Several parameters / thresholds to be set

  28. [Kahl & Henrion] Global L2 Minimum Results indicate in practice, bundle adjustment is a good estimator of global minimum Marr Prize

  29. Techniques @ SIGGRAPH 2005 • Geometric Context from a Single Image • D. Hoiem, A. Efros, M. Herbert • An Iterative Image Optimization Approach for Unified Image Segmentation and Matting • J. Wang, M. Cohen • An Algebraic Approach to Surface Reconstruction from Gradient Fields • A. Agrawal, R. Chellappa, R. Raskar

  30. Miscellaneous • Sparse Image Encoding using a 3D Non-negative Tensor Factorization • T. Hazan, S. Polak, A. Shashua • A Unifying Approach to Hard and Probabilistic Clustering • R. Zass, A. Shashua • A Graph Cut Algorithm for Generalized Image Deconvolution • A. Raj, R. Zabih • Efficiently Registering Video into Panoramic Mosaics • D. Steedley, C. Pal, R. Szeliski

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