DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

Instructor: Serge Belongie, Associate Professor, EBU3B 4118. Office Hours: click here.

Teaching Assistant: Steve Branson Office Hours: M 3:30-4:30 pm EBU3B B240A

Note: when emailing the instructor with questions about the class, please put “cse252c” in the subject line or use the following address:

sjb+cse252c

cs.ucsd.edu

Class section id for CSE252C: #711222. Lecture: MW 2-3:20pm, Room EBU3B 2154.

Class mailing list: Click here.

Class discussion board: http://www.piazzza.com/ucsd/cse252c.

Topics to be Covered: Object detection and recognition; visual category recognition; dataset issues; feature detection and description; object models; statistical pattern recognition based methods.

Prerequisites: linear algebra, calculus, probability and statistics. This course makes extensive use of Matlab. Click here for information on Matlab. Assignments should be prepared using LaTeX. If you are not familiar with LaTeX, click here.

Homework:

• Assignment #1 [tex] [pdf]
• Assignment #2 [tex] [pdf]
• Assignment #3 [tex] [pdf] [make_pointset.m]
• Assignment #4 [tex] [pdf] [generate_data_1.m]
• End of Quarter Exam [pdf]

Lecture Topics, Readings and Scribe Notes:

Part I: Introductory Topics.
• Lecture 1 (Mar. 28): Problem overview, datasets, performance evaluation.
Rabaud et al. (2005), Ch. 2, Pinz (2006), Ch. 1-2, Mundy (2006), Ponce et al. (2006), Fawcett (2004)
• Lecture 2 (Mar. 30): 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. 4): 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. 6): Efficient nearest neighbor methods. [pdf]
Frome & Malik (2006), Shakhnarovich et al. (2003)
Part II: Detectors, Descriptors, Features.
• Lecture 5 (Apr. 11): Basics of image processing. [pdf]
Farid (2001), Lindeberg and ter Haar Romeny (1994), Part I, Perona (1995), Freeman & Adelson (1991)
• Lecture 6 (Apr. 13): Interest point detection.
  Mikolajczyk and Schmid part I (2004), Local Invariant Features Tutorial
• Lecture 7 (Apr. 18): Interest point description.
  Mikolajczyk and Schmid part II (2005)
• Lecture 8 (Apr. 20): Pairwise clustering, spectral graph theoretic grouping. [pdf]
  Shi & Malik (2000), Meila & Shi (2001), Wertheimer (1923)
Part III: Object Models.
• Lecture 9 (Apr. 25): Modeling and estimating geometric transformations. [pdf]
  Bookstein (1989), Feynman (1966), Girosi et al. (1995), Hertz et al. (1991)
• Lecture 10 (Apr. 27): The correspondence problem. [pdf]
  Scott & Longuet-Higgins (1991), Gold, Rangarajan et al. (1997), Fischler & Bolles (1981), Dawes (2005)
• Lecture 11 (May. 2): Recognition without correspondence; bags of features. guest lecturer: Andrew Rabinovich
Nowak & Triggs (2006), Csurka et al. (2004), Lowe (2004), Rabinovich et al. (2007)
• Lecture 12 (May. 4): Shape Matching. [pdf]
  Belongie et al. (2002), Icke (2004)
• (May. 11): Guest Lecture Merrielle Spain. Modeling and predicting object attention in natural scenes.
• Lecture 13 (May 13): 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)
• Lecture 15 (May 18): Kernel PCA, Support Vector Machines. [pdf]
  Schölkopf et al. (1998), Schölkopf et al. (1999), Burges (1998)
• Lecture 16 (May 23): Boosting, AdaBoost, Cascades. [pdf]
  Freund & Schapire (1999), Viola & Jones (2001)
• Lecture 17 (June 1): Final Project Presentations I. [reports]

Related Courses:

• CS 395T, Object Recognition, Spring 2010 Kristen Grauman, UT Austin.
• 16-721 Learning-Based Methods in Vision, Spring 2009 Alyosha Efros, CMU.
• 6.899 Learning and Inference in Vision, Spring 2002 Bill Freeman, MIT.
• CS 323, Understanding Images and Videos: Recognizing and Learning High-Level Visual Concepts, Fall 2009 Fei-Fei Li, Stanford.
• CS 294, Recognizing People, Objects and Actions, Spring 2004 Jitendra Malik, UC Berkeley.
• Recognition Problems in Computer Vision, Summer 2009 Greg Mori, Simon Fraser U.
• 9.912, Scene Understanding Seminar, Fall 2006 Aude Oliva, MIT.
• EE/CNS/CS 148 - Selected Topics in Computational Vision: Visual Recognition, Spring 2006 Pietro Perona, Caltech.
• CIS 680-301, Vision and Learning, Spring 2005 Jianbo Shi, U.Penn.

Most recently updated on Sept. 21, 2007 by Serge Belongie.