ECE 5256 – Syllabus
SYLLABUS*
ECE 5256 – Digital Image Processing
Fall 2005
Instructor Information
Dr. Georgios C. Anagnostopoulos
Office Location:
Tel:
Email:
URL:
Class URL:
Weekly Schedule:
Room 345, Olin Building.
(321)674-7125
georgio@fit.edu
http://my.fit.edu/~georgio/
TBA
http://my.fit.edu/~georgio/about_me/schedule.htm
Catalog Data
ECE 5256 - Digital Image Processing
Credits: 3.00
DIGITAL IMAGE PROCESSING. Investigates image processing by machine for such purposes
as robotics, biomedicine, remote sensing and photogrammetry. Topics include image
enhancement and image analysis, transform techniques including the wavelet transform, feature
extraction, segmentation, compression and morphology. The Khoros graphical interface
programming language is used.
College: College of Engineering
Department: Electrical/Computer Engineer
Restrictions:
Textbook
Digital Image Processing, R.C. Gonzalez & R.E. Woods, 2nd Ed., Prentice Hall,
2000, ISBN 0-201-18075-8.
*
Created August 22nd, 2005.
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ECE 5256 – Syllabus
Course Topics
Digital Image Fundamentals
Visual Perception
Image Sensing & Acquisition
Image Sampling & Quantization
Pixel relationships & distances
Image Enhancement in Spatial Domain
Gray Level Transformations
Histogram Processing
Enhancement via Arithmetic/Logical Operators
Spatial Filters
Image Enhancement in Frequency Domain
Frequency Domain
Fourier Transform & FFT
Frequency Domain Filters
Homomorphic Filters
Image Restoration
Image Degradation Models & Restoration Process
Aperiodic/Periodic Noise Filtering
Position Invariant Degradations
Various Filtering Methods for Restoration
Color Image Processing
Color Models
Pseudocolor Processing
Color Transformations
Smoothing & Sharpening
Morphological Image Processing
Dilation & Erosion
Hit-or-Miss Transformation
Basic Morphological Algorithms
Image Segmentation
Edge Linking & Boundary Detection
Thresholding
Region-based Segmentation
Watershed-based Segmentation
Course Objectives
The goal of the course is to provide a fundamental background on Digital Image
Processing. After the completion of this course the student will have understood
the basic concepts behind the processing of digital images as well as used
various techniques of filtering/processing images. The course serves as the basis
for more advance topics in Computer Vision, such as Object Recognition, Scene
Interpretation, among others.
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ECE 5256 – Syllabus
Student Performance Assessment
There will be only one final, comprehensive, oral exam for this class, 3 major
computer projects and 6-7 sets of homework, which are going to be assigned on
a regular basis. MATLAB will be extensively used for the homework and projects.
Additionally, the projects will entail programming in C & C++. Students are
expected to be proficient with MATLAB and C/C++. Solutions for the homework
problems and exams will be made available on-line a couple of days after the
designated due date.
Grading Scheme
Homework or any other specified deliverable that is going to be turned in after
solutions have been posted will not receive any credit. Also, I intend to follow
FIT’s zero-tolerance cheating policy. The score-weighting scheme for this class
as well as the correspondence of final scores to final letter grades is depicted in
the next two tables.
Score weighting
Homework
Computer Project 1
Computer Project 2
Computer Project 3
Final Oral Exam
15%
20%
20%
20%
25%
Score to Letter Grade conversion
90 and above
A
80-89
B
70-79
C
60-69
D
59 and below
F
Notice
The aforementioned guidelines are approximate; making changes to them is up
to my discretion after sufficient, prior notice is given to you.
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ECE 5256 – Syllabus
Please provide me via email (georgio@fit.edu) with Subject: “ECE 5256: Student
info” by the end of this week with some personal information as requested
below:
LAST NAME:
FIRST NAME:
SSN1:
EMAIL:
PHONE:
(very helpful in case something urgent occurs - Optional)
MAJOR:
(CS, CpE, EE, etc. - Optional)
STUDENT STATUS:
(Undergraduate/Graduate - Optional)
1
Or FIT Student ID Number, if you do not have a SSN assigned.
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