King Saud University
College of Computer and Information Sciences
Department of Computer Science
CSC478 Digital Image Processing and Analysis
(3-0-1) - Elective Course
Catalogue Description
Introduction; Image Sensing and Acquisition; Some Basic Gray Level Transformations for Image Enhancement.; Image Contrast Enhancement Using Histogram Processing; Image Smoothing Using Spatial Filters; Image Sharpening Using Spatial Filters; Point, Line and Edge Detection; Basic image compression procedures; Basic Global and Adaptive Thresholding for Image Segmentation; Optimal Global and Adaptive Thresholding for Image Segmentation; Region-Based Image Segmentation and Edge-Based Segmentation; Image Restoration in the Presence of Noise-Spatial Filtering; Image Enhancement in Frequency Domain; Objects Representation and Description; introduction to Object Recognition
Pre-requisites for this course
Data Structures - CSC 212
Experience with C/C++, JAVA or Matlab
Textbook :
1. Rafael C Gonzalez, Richard E Woods 2nd Edition, Digital Image Processing - Pearson Education 2003.
2. Image Processing Analysis and Machine Vision – Millman Sonka, Vaclav hlavac, Roger Boyle, Broos/colic.
Required software: The Mathworks, The Student Edition of MATLAB, Release 2006a or later.
Course Objectives:
The course aims are:
To study the image fundamentals and mathematical transforms necessary for image processing.
To study the image enhancement techniques
To study the basic image compression procedures.
To study the image thresholding and segmentation techniques.
To study introduction to Object Recognition.
Course Learning Outcomes
Upon completing CSC478, students should have the following capabilities:
1. Have an appreciation of the fundamentals of Digital image processing including the topics of filtering, transforms and morphology, and image analysis and compression.
2. Describe Image segmentation and pattern recognition
3. Be able to implement basic image processing algorithms
4. Have a the skill base necessary to further explore advanced topics of Digital Image Processing
Evaluation of Student Performance
Projects: 20% Midterm exam: 40% Final Exam: 40%
Expected Performance Criteria:
The student will solve programming & theoretical problems and take a final exam.
Topics
· Digital Image Fundamentals
· Intensity Transformations and Spatial Filtering
· Filtering in the frequency domain
· Color Image processing
· Introduction to Image compression
· Morphological image processing
· Image segmentation
· Introduction to image representation and description
· Introduction to object recognition
Schedule:
15 weeks of 3 one-hour lectures and 1 one-hour tutorial
Relationship of Course to ABET Criteria:
Criterion 2 - Program Educational Objectives:
This course allows the student to gain the necessary skill and experience to contribute to either a research or development project
Criterion 3 - Program Outcomes:
a- an ability to apply knowledge of mathematics, computing, science, and engineering appropriate to the discipline
the student use his skills in programming and mathematicsto implement various image processing algorithms.
b- an ability to analyze a problem, and identify and define the computing requirements appropriate to its solution
the student should use the appropriate techniques to do segmentationand recognition
c- an ability to design, implement and evaluate a computer-based system, process, component or program to meet desired
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d- an ability to function effectively on teams to accomplish a common goal
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e- an understanding of professional, ethical, legal and social issues and responsibilities
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f- an ability to communicate effectively
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g- an ability to analyze the local and global impact of computing on individuals, organizations and society, including ethical, legal, security and global policy issues
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h- a recognition of the need for, and an ability to engage continuing professional development
preparing the student to use image processing techniques professionally.
i- an ability to use the current techniques, skills, and tools necessary for computing practice.
Using the current techniques, skills, and tools necessary forimage processing and analysis.
j- an ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices.
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k- an ability to apply design and development principles in the construction of software systems of varying complexity
the student will design and develop programs to construct an image processing and analysis software to solve problems with various complexity
Support of Program Outcomes by Course Outcomes
The relationship shown in Table1. This course meets CS/ABET’s outcomes a, b, h, i and k
|
|
Program Outcomes |
(a) |
(b) |
(h) |
(i) |
(k) |
|
Course Learning Outcomes |
|
30% |
20% |
15% |
20% |
15% |
|
1 |
|
|
|
|
X |
|
|
2 |
|
|
X |
|
|
|
|
3 |
|
X |
|
|
|
X |
|
4 |
|
|
|
X |
|
|
|
