Lectures

This is a tentative outline for the course. Changes may be made as the course progresses, and we will update the outline accordingly.

Part I: Introductory Topics

• Lecture 1 (Apr. 2): Problem overview, datasets, performance evaluation. [pdf]

• Rabaud et al. (2005), Ch. 2, Pinz (2006), Ch. 1-2, Mundy (2006), Ponce et al. (2006), Fawcett (2004)

• Lecture 2 (Apr. 4): Images as vectors, distance-based classification, clustering. [pdf]

• Golub & Van Loan (1996), Sec. 2.2, Lewis (1995), Jain & Dubes (1988), Ch. 2, Tuzel & Meer (2002)

• Lecture 3 (Apr. 9): Working with distributions, histograms-of-X, comparing distributions. [pdf]

• Rubner et al. (2001), Schiele & Crowley (2000), Swain & Ballard (1991), Porikli (2005), Topsøe (2000)

• Lecture 4 (Apr. 11): Efficient nearest neighbor methods. [pdf]

• Frome & Malik (2006), Shakhnarovich et al. (2003)

Part II: Detectors, Descriptors, Features

• Lecture 5 (Apr. 16): Basics of image processing. [pdf]

• Farid (2001), Lindeberg and ter Haar Romeny (1994), Part I, Perona (1995), Freeman & Adelson (1991)

• Lecture 6 (Apr. 18): Interest point detection.

• Mikolajczyk and Schmid part I (2004), Local Invariant Features Tutorial

• Lecture 7 (Apr. 23): Interest point description.

• Mikolajczyk and Schmid part II (2005)

• Lecture 8 (Apr. 25): Pairwise clustering, spectral graph theoretic grouping. [pdf]

• Shi & Malik (2000), Meila & Shi (2001), Wertheimer (1923)

Part III: Object Models

• Lecture 9 (Apr. 30): Modeling and estimating geometric transformations. [pdf]

• Bookstein (1989), Feynman (1966), Girosi et al. (1995), Hertz et al. (1991)

• Lecture 10 (May 2): The correspondence problem. [pdf]

• Scott & Longuet-Higgins (1991), Gold, Rangarajan et al. (1997), Fischler & Bolles (1981), Dawes (2005)

• Lecture 11 (May 7): Recognition without correspondence; bags of features.

• Nowak & Triggs (2006), Csurka et al. (2004), Lowe (2004), Rabinovich et al. (2007)

• Lecture 12 (May 9): Shape Matching. [pdf]

• Belongie et al. (2002), Icke (2004)

• Lecture 13 (May 14): Constellation and Part-based Models. [pdf]

• Geman et al. (1997), Fergus (2007)

Part IV: Statistical Pattern Recognition

• Lecture 14 (May 16): Learning mixture distributions, the EM algorithm. [pdf]

• Bilmes (1998), Tomasi (2004)

  • No Lecture on May 21 due to SoCal Vision Meetup.

• Lecture 15 (May 23): Kernel PCA, Support Vector Machines. [pdf]

• Schölkopf et al. (1998), Schölkopf et al. (1999), Burges (1998)

  • No Lecture on May 28 due to Memorial Day.

• Lecture 16 (May 30): Boosting, AdaBoost, Cascades. [pdf]

• Freund & Schapire (1999), Viola & Jones (2001)

Final Project Presentations

• June 4: Final Project Presentations I.

• June 6: Final Project Presentations II.