CSE190-B

Introduction to Computer Vision

Tuesday, Thursday 3:30-4:50

Warren Lecture Halls, Rm. 2205

Class Mailing list:

Announcement: Final Exam, Thursday, June 12, 3:00-6:00, WLH 2205.

Instructor: David Kriegman

Office: AP&M 3101

Phone: (858) 822-2424

Email: kriegman@cs.ucsd.edu

Office Hours:    Wednesday1:00-2:30

TA: Diem Vu

Location: AP&M 3349D

Email: d1vu@cs.ucsd.edu

Office Hours: Tuesday11:00-12:30, Thursday 10:00-11:00

Class Description: The goal of computer vision is to compute properties of the three-dimensional world from images and video.Problems in this field include identifying the 3D shape of a scene, determining how things are moving, and recognizing familiar people and objects.This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, object recognition, and 3D shape reconstruction through stereo, photometric stereo, and structure from motion.4 units.

Required Text: Introductory Techniques for 3-D Computer Vision, E. Trucco and A. Verri, Prentice Hall, 1998.

Prerequisites: Linear algebra and Multivariable calculus (e.g., Math 20A & 20F), data structure/algorithms (e.g., CSE100), a good working knowledge of C,C++, or Matlab programming.

Assignments:     45%

Midterm:          20%

Final Exam:       35%

Late Policy: Written homework will be due in class and accepted thereafter with a penalty of 10% per day starting from the due date. Programming assignments will have a hand-in procedure described with the assignment, and also has a 10% per day late penalty. No assignments will be accepted after the graded assignments have been returned or the solutions have been released.

Assignments

Syllabus

[ Note that this Syllabus is tentative & subject to change]

 Week Date/ Link to lecture notes Topic/Readings 1 Intro to Computer Vision / T&V Chapter 1 Human Visual System 2 Image Formation, T&V pp.15-19 Color, Color is well-treated in many image-processing texts.Some reasonable on-line sources include: Basics of Color, A FAQ on Color 3 Segmentation & Binary images, Horn Chapters 3&4, available at e-reserves See on-line resource Binary Images &/Filtering 4 Filtering, T&V pp. 55-63 Canny Edge detection, T&V 67-81 5 Curves, Hough Transforms, T&V, pp. 97-100 Intro to Shape-from-x, MidtermReview 6 May 6 Midterm Photometric Stereo ,T&V pp. 140-171 7 Stereo I,  T&V pp. 140-171 Stereo II 8 Epipolar Geometry Discrete structure from Motion , T&V pp.195-202, 208-211 9 Optical Flow, T&V pp. 178-194 10 Statistical Pattern Recognition, T&V pp. 248, 262-269 Appearance-based recognition, “Finding Templates Using Classifiers”, Forsyth & Ponce Model-based recognition/Final Exam Review, , T&V 249-261