Schedule

  • review the prerequisites, Matlab Tutorial , Prerequisites review (.pdf) , part1.pdf , part2.pdf , Linear Algebra review (S. Mallat) .pdf
  • Principal Component Analysis, statistical and geometric derivation (homework 1)
  • Fisher Discriminant Analysis, Indenpendent Component Analysis (homework 2, 3)
  • Generalized Component Analysis (homework 4)
  • ISOMAP, Multidimensional Scaling, Dynamic textures
  • Temporal and Spatial Factorization for reconstruction and segmentation
  • Tracking and Conditional Density Propagation
  • Segmentation, Graph-Based methods
  • Hidden Markov Models
  • Object Detection, Object/Cathegory Recognition

    Handouts
    Eigenfaces Notes (.pdf)
    Soft K-means (.pdf)

    Preliminary List of Papers

    1. M. Turk and A. Pentland. Eigenfaces for Recognition.
      Journal of Cognitive Neuroscience, 3(1), 1991. (.pdf) , Homework 1 (due February 15).
    2. P. N. Belhumeur, J.P. Hespanha, D.J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection.
      IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 711-720, 1997. (.pdf) Homework 2 (due February 21).
    3. S. Mika, B. Scholkopf, A. Smola et. al. Kernel PCA and De-Noising in Feature Spaces.
      NIPS 1999. (.pdf)
    4. Image Representations. A. Bell and T. Sejnowski: Independent Components of natural scenes are Edge Filters. (link)
    5. H. Farid and E.H. Adelson. Separating Reflections from Images by use of Independent Components Analysis.
      JOSA, 16(9):2136-2145, 1999. (.pdf) , Homework 3 (due March 1).
    6. R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis.
      IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2003. (.pdf) , (.pdf) Homework 4 (due March 15).
    7. J. Tennebaum and V. De Silva and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction.
      Science 290. (2000). (.pdf)
      R. Pless. Image Spaces and Video Trajectories.
      International Conferene on Computer Vision, 2003 (.pdf)
      S. Soatto, G. Doretto, Y.-N.Wu. Dynamic Textures.
      International Journal of Computer Vision. (.pdf)
    8. C. Tomasi and T. Kanade. Factoring Image Sequences into Shape and Motion.
      Proceedings of IEEE Workshop on Visual Motion, 1991. (.pdf) ,
      S. Baker et. al. Lucas-Kanade 20 year on. CMU-tech-report, 2003. (.pdf) , Homework 5 (due March 29).
    9. Selected papers on non-rigid and multiple motion estimation (.html)
    10. Object Tracking and Particle filtering.
    11. M. Isard, A, Blake. Condensation: Conditional Density Propagation for visual tracking, IJCV 98 (.html) .
      T. Cham and J. Regh. Multiple Hypothesis Approach to Figure Tracking, CVPR 99 (.html) .
    12. J. Shi and J. Malik. Normalized Cuts and Image Segmentation.
      IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905, 2000. (.html) , Homework 6 (due April 5).
    13. P. Felzenszwalb and D. Huttenlocher. Efficient Matching of Pictorial Structures. CVPR 2000.
      S. Ioffe and D. Forsyth. Finding people by Sampling, ICCV 1999.
      J. Kosecka, F. Li. Vision Based Markov Localization, (.pdf)
    14. Object Detection, Object Recognition, Constelation of features models (selected papers)
      Fergus, R. , Perona, P. and Zisserman, A. Object Class Recognition by Unsupervised Scale-Invariant Learning, CVPR 2003. (.pdf)
      C. Schmid. A Structured Probabilistic Model for Recognition, CVPR 1999. (.html)
      H. Schneiderman, T. Kanade. "A Statistical Method for 3D Object Detection Applied to Faces and Cars". IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000) (.html)
      Paul Viola, Michael Jones. Robust Real-time Object Detection (2001), International Journal of Computer Vision (.html)
    15. D. Lowe. Distintive Image Features from scale-invariant keypoints.
      International Journal of Computer Vision, 2004. (.pdf)
    16. Y. Weiss. Deriving Intrinsic Images from Image equences.
      International Conference on Computer Vision, 2001 (.html)

    Addional list of topics:

  • Probabilistic Object Recognition - Constellation of Features Models
  • Tracking and Causal Motion Estimation
  • Object detection and Modelling context
  • Low-Level feature invariants
  • Modelling reflectance properties of scenes

  • Recommended textbooks (in addition to links provided on the web page)
    1. G. Strang: Linear Algebra and its applications
    2. E. Kreyzig: Advanced Engineering Mathematics