National University of Sciences and Technology
Nust Home
ALUMNI
Contact Us
Home
All Courses
Home
>
Courses Detail
Home
|
Back
EC-835 Digital Image Processing
Campus
College of E&ME
Programs
PG
Session
Fall Semester 2016
Course Title
Digital Image Processing
Course Code
EC-835
Credit Hours
03
Pre-Requisutes
Digital Image Processing by Rafael C. Gonzalez, Richard E. Woods, Addison Wesley, 3rd Ed. 2008.
Basic knowledge about linear algebra, matrices and complex variables.
Concepts related to signals and systems and digital sig
Course Objectives
To develop thorough understanding of digital image processing fundamentals.
To explore the properties of discrete transforms and their importance with respect to image processing.
To study various image enhancement techniques in spatial and frequency domains, fundamentals of image compression, introduction to color image processing, wavelets and morphological image processing.
To have a basic understanding of some topics of machine learning to combine these ideas with image processing techniques to produce good projects.
Detail Content
Grading:
Sessional Exams: 25%.
Quizzes (4-6): 10%.
Computer and numerical assignments: 10%.
Final Project: 15%.
Final Exam: 40%.
Quizzes:
Ten-minute surprise quizzes will be given periodically.
Computer Assignments & Project:
For computer assignments, sharing of programming tips and discussing general concepts is allowed. Collaborating on program writing is not. The term-project will be conducted in groups. One to three students can form a group. It is very important to have hands on image processing algorithms. Almost 25% marks are directly related to it inform of computer assignments and projects etc. The project will be monitored throughout the semester on periodic basis and marks will be assigned accordingly.
Topical Outline:
Introduction to Image processing
Image processing Fundamentals
Image Enhancement & Restoration
Morphological operations
Feature extraction (edges, corners)
Segmentation
Texture analysis
Wavelets
Introduction to Machine Learning and basic types of classifiers, Performance parameters for evaluation
Text/Ref Books
Digital Image Processing Using Matlab by Rafael C. Gonzalez and Richard E. Woods, Pearson Education, 2009.
Digital Image Processing by Kenneth R. Castleman, Prentice Hall International Edition, 1996.
http://www.imageprocessingplace.com/
Time Schedule
Fall 2015
Faculty/Resource Person