The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Course Name: Pattern Recognition
Course Code: COMP 422
I. Basic Course Information
Program(s) on which the course is given: Computer Engineering
Core or elective element of program: Core
Department offering the course: Electrical and Electronics Engineering- Computer Dept
Academic level:4
Semester in which course is offered: Spring
Course pre-requisite(s): Advanced Programming C++
COMP 412
Credit Hours:3
Contact Hours Through: 5
Lecture
2.0
Tutorial*
1.0
Practical*
2.0
Total
5.0
Approval date of course specification: September 2013
II. Overall Aims of Course
Upon completion of this course, students will be able to:
-Understand pattern recognition methods and related data analysis applications.
-Practice mathematical principals such as linear algebra, probabilities and statistic.
-Understand the components in common pattern recognition systems and the stages in
system design cycle.
-Analyze a practical pattern recognition problem, provide an effective system solution,
and assess the expected system performance.
-Contribute to the advances in the field of pattern recognition.
III. Program ILOs covered by course
Program Intended Learning Outcomes (By Code)
Knowledge &
Intellectual Skills
Professional Skills
Understanding
K4, K5, K11, K12
I1, I3 ,I5, I6, I9,
I10
P1, P4, P6
General
Skills
G1, G3, G4
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The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
IV. Intended Learning Outcomes of Course (ILOs)
a. Knowledge and Understanding
On completing the course, students should be able to:
k1. Relate practical application of theories in different fields through projects and field
studies.
k2. Express unique oriented Knowledge in the relevant fields.
k3. Recognize principles and methods of design used in electrical and electronic engineering.
k4. Recognize principles and methods of design used in computer engineering.
b. Intellectual/Cognitive Skills
On completing the course, students should be able to:
i1. Use appropriate knowledge and skills to identify, formulate, analyze, and solve complex
engineering problems in order to reach substantiated conclusions.
i2. Use brainstorming and innovation techniques to deal with problems and to develop new
ideas.
i3. Solve and investigate complex problems by methods that include appropriate experiments,
analysis and interpretation of data, and synthesis of information in order to reach valid
conclusions.
i4. Use and develop computer programs.
i5. Apply solutions for complex, open-ended engineering problems.
i6. Apply appropriate computer based methods for modelling and analyzing problems in
electrical and electronic engineering.
i7. Use software engineering techniques in program development.
c. Practical/Professional Skills
On completing the course, students should be able to:
p1. Formulate and use the appropriate mathematical methods for modelling and analyzing
problems in electrical, electronic and communications engineering.
p2. Collect information and develop new ideas.
p3. Design, build and test a communication system.
d. General and Transferable Skills
On completing the course, students should be able to:
g1. Manipulate, sort and present the information in a variety of ways.
g2. Use of general IT tools.
g3. Express creativity and innovation in problem solving and working with limited or
contradictory information.
V. Course Matrix Contents
Main Topics / Chapters
Pattern Recognition:
1- Numerical – Symbolic and
structural
2- Components of a numerical
Duration
(Weeks)
Course ILOs Covered by Topic
(By ILO Code)
K&U
I.S.
P.S.
G.S.
2
k1
i1, i2
2
k2, k3
i1
2
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
34567-
pattern recognition system
Process description
Property analysis
Segmentation
Image processing applications
Image mosaics
Net Teaching Weeks
2
2
2
2
1
13
k4
All
i3
i4
i5
p2
p1
p3
All
g1
i6, i7
VI. Course Weekly Detailed Topics / hours / ILOs
Week
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Sub-Topics
Total
Hours
Introduction to pattern recognition
2
methods
Pattern Recognition: Numerical –
5
Symbolic and structural
Components of a numerical pattern
recognition system: process explanation
5
– property analysis – discriminator
design – massive analysis
Components of a numerical pattern
recognition system: process explanation
5
– property analysis – discriminator
design – massive analysis
Process description: symbolic –
5
numerical – implicit – noisy – rule based
Process description: symbolic –
5
numerical – implicit – noisy – rule based
Midterm Exam
Property analysis: pre processing –
5
property conclusion
Segmentation: Bayez Theorem for
decision making – 2D segmentation –
5
segments – special functions – decision
surface – Bayez segment
Segmentation: Bayez Theorem for
decision making – 2D segmentation –
5
segments – special functions – decision
surface – Bayez segment
Image processing applications: image
analysis - Interpreting the 3D world from
5
image data
Image processing applications: image
analysis - Interpreting the 3D world from
5
image data
Motion estimation
5
Image mosaics. 3D-shape reconstruction.
5
Contact Hours
Theoretical
Practical
Hours
Hours*
2
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
2
3
3
3
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
15
Object recognition, and image retrieval
Final Exam
Total Teaching Hours
VII. Teaching and Learning Methods
Teaching/Learning
Method
Lectures & Seminars
Tutorials
Computer lab Sessions
Practical lab Work
Reading Materials
Web-site Searches
Research & Reporting
Problem Solving /
Problem-based Learning
Projects
Independent Work
Group Work
Case Studies
Presentations
Simulation Analysis
Course ILOs Covered by Method (By ILO Code)
K&U
All
Intellectual
Skills
All
All
Professional
Skills
All
All
General
Skills
All
All
All
All
Others (Specify):
VIII. Assessment Methods, Schedule and Grade Distribution
Course ILOs Covered by Method
(By ILO Code)
Assessment
Method
K&U
I.S.
P.S.
G.S.
Midterm Exam
k1, k2
Final Exam
All
Quizzes
All
Course Work
All
Report Writing
Case Study
Analysis
Oral
Presentations
Practical
Group Project
Individual Project
i1, i2, i4
All
All
Assessment
Weight /
Percentage
Week
No.
20 %
All
All
10 %
5%
All
All
10 %
Others (Specify):
4
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
IX. List of References
Essential Text Books
Course notes
Recommended books
Periodicals, Web sites,
etc …
Pattern Classification, Second Edition, Richard O. Duda, Peter E.
Hart, David G. Stork
ISBN #: : 0-471-05669-3
Course Management System CMS
X. Facilities required for teaching and learning
big sized lecture rooms - computers (Personal & Notebook) - data show
Course coordinator: Associate Professor. Nahla El Araby
Head of Department: Associate Professor/ Tamer AbdelRahman
Date: September 2013
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