Computer Vision and Artificial Intelligence
Gang Hua, Ph.D.
Principal Researcher/Research Manatger
Microsoft Research Asia
firstnamelastname AT gmail.com
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[Vision Lab][Teaching] @Stevens Institute of Technology
CS 558 Computer Vision
Term: Spring 2014Instructor: Prof. Gang Hua
Time: Tuesday 6:15pm – 8:45pm
Building/Room: BC 320
Office Hour: Wednesday 4:00pm—5:00pm by appointment
Office Hour Location: Lieb Building/Room 305
Course Assistant: Benjamin Abruzzo
Course Assistant:
Course Website: http://www.cs.stevens.edu/~ghua/ghweb/teaching/CS558Spring2014.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% .
|
Week |
Date |
Topic |
Reading |
Homework |
Slides |
|
1 |
01/14/2014 |
Introduction to Computer Vision |
Szeliski Ch1 & Matlab Tutorial |
||
| 2 | 01/21/2014 | No class (canceled due to weather) | |||
| 3 | 01/28/2014 | No class (Prof. Hua is Traveling) | |||
| 4 | 02/04/2014 |
Image Formation: Cameras |
Szeliski Ch. 2.1 |
Project #0 due, HW#1 Out ( Camera ) |
Lecture II |
|
5 |
02/11/2014 |
Image Formation: Light, Shade, and Color |
Szeliski 2.2 & 2.3 |
HW#1 Due, HW#2 Out ( Light ) |
Lecture III |
| 6 | 02/18/2014 | No class (Monday schedule) | |||
|
7 |
02/25/2014 |
Convolution, Filtering, and Edge Detection |
Szeliski 3.2, 4.2 |
HW#2 Due, Project #1 Out ( Hybrid Image ) |
Lecture IV |
|
8 |
03/04/2014 |
Segmentation and Grouping |
Szeliski 5 |
|
Lecture V |
| 9 |
03/11/2014 |
No class (Spring break) |
|
||
|
10 |
03/18/2014 | Features: Corner&Blob Detection, Descriptor | Szeliski 4.1 | Project #1 Due, Project #2 ( Local Features ) | Lecture VI |
|
11 |
03/25/2014 |
Fitting: Line Fitting,RANSAC, Hough Transform |
Szeliski 4.3.2 |
|
Lecture VII |
|
12 |
04/01/2014 |
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 |
|
13 |
04/08/2014 |
Object Recognition (1) |
|||
|
14 |
04/15/2014 |
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/ |
|
|
Stereo Vision: Bi-nocular and Multi-view Stereo |
Lecture XI | ||||
|
15 |
04/22/2014 |
Face Detection and Recognition |
|
Lecture XII | |
|
16 |
04/29/2014 |
Motion: Visual Tracking and Optical Flow |
|||
|
17 |
05/06/2014 |
Final Presentation and Competition & Final Report Due |
Project #4 Presentation & Results Due |