Machine Vision English Syllabus

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Machine Vision

631 D8200 SYLLUBUS


Fall Semester, 2003


Instructor:

Ta
-
Te Lin
林達德

Office: Rm. 203, Main Office Building, BIME

Telephone and Fax: (02) 23929416

E
-
mail:
m456@ccms.ntu.edu.tw

Website: http:/ww
w.bime.ntu.edu.tw/~ttlin


TA:

TBA (TA E
-
mail)


Course Homepage:

http:/www.bime.ntu.edu.tw/~ttlin/machine_vision.htm


Course Description:

The aim of this course is t
o introduce the theory, applications and techniques of machine vision

to
students, and

t
o p
rovide students with an understanding of the problems involved in the
development of
machine
vision systems. Th
e

course

first introduces

the “low
-
level” algorithms of
image processing

that are necessary for the

mid
-
level


vision or feature extraction.
It

continues
with “high
-
level” algorithms
such as

pattern recognition
,

and
3D
analysis
and modeling of
objects
and scenes. This course will also lay emphasis on the
practical
integration of machine vision

systems, and the related applications in bio
-
machatro
nics.


Textbook:

Shapiro, L. G. and G. C. Stockman. 2001. Computer Vision. Upper Saddle River,
New Jersey: Prentice
-
Hall, Inc.


Recommended Reading:

1.

Davis, E. R. 1997. Machine Vision. 2
nd

Ed. San Diego, California: Academic Press.

2.

Jain, R. J., R. Kasturi a
nd B. G. Schunck. 1995. Machine Vision. New York: McGraw
-
Hill, Inc.

3.

Haralick, R. M. and L. G. Shapiro. 1992. Computer and Robot Vision. Vol. 1 & 2. Reading,
Massachusetts: Addison
-
Wesley Publishing Company, Inc.

4.

Faugeras, O. 1999. Three
-
Dimensional Compute
r Vision: A Geometric Viewpoint. Cambridge,
Massachusetts: The MIT Press.


Assignments, Projects and Grading
:

There will be
bi
-
weekly homeworks, consisting of programming assignments and/or written
assignments. Late homeworks will be penalized
1
0% for each

day they are late. No late
assignments will be accepted after sample solutions are posted.
E
xtension for an assignment

can
be granted if you

ask for permission before the due date.
There will be a mid
-
term exam and a
final exam based mainly on the materia
ls in the textbook and lectures.
The
term

project will be
an independent project on a fun topic of your choice. Besides programming,
the project

also
involve
s

analyzing and
summarizing results in a written report and a presentation to classmates.
The
final

grade will be based on
:


Homework Assignments


40%

Mid
-
Term Exam




20%

Final Exam





20%

Term Project




20%


Pre
-
Requisites:

Good programming experience

(Visual C++, Borland C++, Matlab, Visual Basic, etc.)

and
completion of an introductory image proc
essing course
.


Schedule of Lectures:

Thursdays
2
:
1
0
-
5
:
00 PM

Date

Topics

Reading

Feb. 20

Introduction

Chapter 1

Feb. 27

Fundamentals of Image Processing

Chapter 2

Mar. 6

Binary Image Analysis

Chapter 3

Mar. 13

Pattern Recognition Concept

Chapter 4

Mar
. 20

Filtering and Enhancing Images

Chapter 5

Mar. 27

Color, Shading and Texture

Chapter 6, 7

Apr. 3

Content
-
Based Image Retrieval

Chapter 8

Apr. 10

Motion from 2D Image Sequence

Chapter 9

Apr. 17

Mid
-
Term Exam


Apr. 24

Image Segmentation

Chapter 10

May 1

Matching in 2D

Chapter 11

May 8

Perceiving 3D from 2D Images

Chapter 12

May 15

3D Sensing and Object Pose Computation

Chapter 13

May 22

Models and Matching in 3D

Chapter 14

May 29

Virtual Reality

Chapter 15

Jun. 5

Integration of a Machine Visio
n System

Course Notes

Jun. 12

Case Studies

Chapter 16 and Course
Notes

Jun. 19

Final Exam and Project Presentation