CS 558 Computer Vision

Term: Spring 2013
Instructor: Prof. Gang Hua
Time: Wednesday 6:15pm – 8:45pm
Building/Room: BC 110　
Office Hour: Wednesday 4:00pm—5:00pm by appointment
Office Hour Location: Lieb Building/Room 305
Teaching Assistant: Haoxiang Li
Course Assistant: Lishuo Zhuang
Course Website: http://www.cs.stevens.edu/~ghua/ghweb/teaching/CS558Spring2013.htm

Course Overview:
Research on computer vision strives for building a machine that can see, which refers to the visual perception process of sensing the scene/object geometry, and recognizing the the scene/object/action/activities from a single or a set of images or a video clip. In this course, we will explore various fundamental topics in this area, including image formation, feature detection, segmentation, multiple view geometry, recognition and learning, and visual motion analysis. This course is intended for graduate students and senior undergraduate students.

Prerequisites:
CS 590 or CS 385 or CS 182, and MA 232

Text Books:
Richard Szeliski, "Computer Vision: Algorithms and Applications", Springer, (Required) (PDF downloadable version at http://szeliski.org/Book )

Grading:
The students will be graded based on course participation (10%), two written homework 10% (5% each), 4 Course Projects ( Project #0 -- 5%, Project #1 -- 10%, Project #2--10%, Project #3 -- 15% ) and a Final Project (Project #4 -- 10% competition, 5% presentation, 25% final report). Final grade: A-- 90% to 100%; B--80% to 89%; C-- 70% to 79%; D--60% to 69%; F -- < 60% .

Schedule:

Week	Date	Topic	Reading	Homework	Slides
1	01/16/2013	Introduction to Computer Vision	Szeliski Ch1 & Matlab Tutorial	Project #0 ( Mini Matlab Project )	Lecture I
2	01/23/2013	Image Formation: Cameras	Szeliski Ch. 2.1	Project #0 due, HW#1 Out ( Camera )	Lecture II
3	01/30/2013	Image Formation: Light, Shade, and Color	Szeliski 2.2 & 2.3	HW#1 Due, HW#2 Out ( Light )	Lecture III
4	02/06/2013	Convolution, Filtering, and Edge Detection	Szeliski 3.2, 4.2	HW#2 Due, Project #1 Out ( Hybrid Image )	Lecture IV
5	02/13/2013	Segmentation and Grouping	Szeliski 5		Lecture V
6	02/20/2013	Features: Corner&Blob Detection, Descriptor	Szeliski 4.1	Project #1 Due, Project #2 ( Local Features )	Lecture VI
7	02/27/2013	Fitting: Line Fitting,RANSAC, Hough Transform	Szeliski 4.3.2		Lecture VII
8	03/06/2013	Visual Geometry: Single view and Epipolar Geometry	Szeliski 7.1 & 7.2	Project #2 Due, Project #3 Out ( Epipolar )Images for P3, by Courtesy of K. Grauman	Lecture VIII
	03/13/2013	Spring Recess
9	03/20/2013	Object Recognition (1)			Lecture IX
10	04/03/2013	Object Recognition (2)	Szeliski 10.3 ICCV 2009 Short Course on Object Recognition	Project#3 Due, Project #4 Out (Recognition) Dataset: [Graz02]Original source from http://www.emt.tugraz.at/~pinz/data/GRAZ_02/	Lecture X
11	04/03/2013	Stereo Vision: Bi-nocular and Multi-view Stereo			Lecture XI
12	04/10/2013	Face Detection and Recognition			Lecture XII
13	04/17/2013	Motion: Visual Tracking and Optical Flow
14	04/23/2013	Q&A for Final Project
15	05/08/2013	Final Presentation and Competition & Final Report Due		Project #4 Presentation & Results Due

Computer Vision and Artificial Intelligence

Gang Hua, Ph.D.

Principal Researcher/Research Manatger

Microsoft Research Asia

firstnamelastname AT gmail.com

CS 558 Computer Vision