DEPARTMENT OF COMPUTER ENGINEERING

College of Electrical and Mechanical Engineering (CEME)

National University of Sciences and Technology (NUST)

1. Course Information
Course Number and Title: EC-312 Digital Image Processing
Credits: 3 (2+1)
Instructor(s)-in-charge: Dr. Muhammad Usman Akram
Course type: Lecture + Lab
Required or Elective: Required
Course pre-requisites None
Degree and Semester DE-36, Semester 6
Month and Year Spring 2017

2. Course Schedule
Lecture: 2 hrs/week, Meets once weekly
Lab: 3 hrs/week, Meets once in a week
Discussion: 1 hr/discussion, multiple discussion sections offered per quarter
Outside study: 3 hrs/week
Office Hours : 3 hrs/week by instructor, 3 hrs/week by teaching assistant/lab engineer

3. Course Assessment
Exam: 2 Sessional and 1 Final
Home work: 5 Assignments
Lab reports: 16 reports
Design reports: 2 Design reports based on Mini and Semester Project
Quizzes: 4 Quizzes
Grading: Quizzes: 8%
Assignments: 7%
2 One Hour Tests (OHTs): 20%
Final Exam: 40%
Lab: 10%
Tasks + Semester Project: 15%
 4. Course book and Related Course Material
Textbooks: 1. Digital Image Processing by Rafael C. Gonzalez and Woods, 3rd
Edition, 2008
Reference Books: 1. Fundamentals of Digital Image Processing: A Practical Approach
with Examples in Matlab by Chris Solomon, Wiley-Blackwel, 2011
2. Digital Image Processing Using Matlab by Rafael C. Gonzalez and
Richard E. Woods, Pearson Education, 2009.
3. Digital Image Processing by Kenneth R. Castleman, Prentice Hall
International Edition, 1996.
4. http://www.imageprocessingplace.com/

5. Catalog Descriptions
This course is consists of topics related to image processing from introductory to a bit advanced level.
The contents include introduction to image processing systems and applications, Image enhancement in
spatial and frequency domains, removal of noise using image restoration, analysis of images using
wavelets, image compression, shape based analysis using morphological operations, thresholding and
clustering based segmentation, feature extraction such as edges, corners and texture based features and
image classification. All lectures are supplemented by home works and laboratory implementations of
image processing tasks using Python, OpenCV and MATLAB

6. Course Objectives
a) The main objective of this course is to provide a comprehensive presentation of the
fundamentals of image processing and analysis both from a theoretical as well as
practical point of view.
b) To familiarize the students with the techniques of image enhancement in spatial and
frequency domain.
c) To introduce the students to the image restoration techniques.
d) To familiarize students with the basic concepts relating to the color image processing.
e) To provide broader understanding of image compression, image morphology and
wavelets.
f) To give them an idea about low and high level feature extraction from images and to
apply classification in order to make decision support system for image processing
based applications
g) To enable students to implement all theoretical information gained during the lectures
in Python or MATLAB and also to program solutions in Python or MATLAB to
practical problems.
h) To give basic knowledge of using camera module along with Raspberry Pi for real
time image processing and computer vision based project
 7. Topics covered in the Course and Level of Coverage
1. Introduction to image processing and it fundamentals 4 hrs
a. Structure of eye
b. Digital image acquisition model
c. Different types of images
2. Image enhancement in spatial domain 5 hrs
a. Intensity transformations
b. Histogram and its analysis
c. Convolution and spatial filtering
3. Image enhancement in frequency domain 3 hrs
a. Basic concepts related to Fourier transform
b. Sampling in frequency domain and introduction to DFT
c. Filtering in frequency
4. Image restoration 2 hrs
a. Introduction to restoration model
b. Different types of noises and their models
c. Image restoration in spatial and frequency domains
5. Color image processing 1.5 hrs
a. Formation of color image
b. Different color models
c. Analysis of colored images
6. Image compression 1.5 hrs
a. Compression models, compression ratio, types of redundancy
b. Variable length coding
c. Lossy and lossless compression
7. Introduction to wavelets 2 hrs
a. motivation of wavelets
b. Wavelet decomposition
c. Haar wavelet
8. Morphological operations for binary and gray images 3 hrs
a. Introduction to morphological operations
b. Morphological operation for binary images
c. Gray level morphological operations
9. Segmentation using thresholding and clustering 2 hrs
a. Global, local and adaptive thresholding
b. K-means and mean shift clustering
10. Feature extraction (edges, corners, texture based features) 6 hrs
11. Classification 2 hrs
12. Design problems and application examples Outside study
 8. Lab Experiments
Lab 01 Introduction to OpenCV and Numerical Python,

Lab 02 Core Operations on Images

Lab 03 Labeling and Connectivity

Lab 04 Image Transformations, Histogram

Lab 05 Histogram Based Image Enhancement, , Histogram Equalization,

Lab 06 Thresholding, Convolution, Smoothing, Sharpening, noise removal

Lab 07 Mini project Vivas

Lab 08 Open Lab 1

Lab 9 Edge Detection, Gradients magnitude and phase, Frequency Domain analysis and Filtering

Lab 10 Gaussian and butterworth filters in frequency domain, Color based segmentation

Lab 11 Morphological operations on images

Lab 12 Introduction to Raspberry Pi and video analysis on Pi

Lab 13 Raspberry Pi Face Recognition and tracking

Lab 14 Raspberry Pi color based object detection

Lab 15 Raspberry Pi hand gesture detection

Lab 16 Open Lab II

9. CourseOutcomesandtheirRelationtoProgramOutcomes (Mapping
CLO to PLO)
Course Learning Outcome (CLOs) Learning
PLOs Level

CLO 1 Understanding the fundamentals and basic concepts of image PLO 1,

processing related to image segmentation, compression, enhancement PLO 2 C2
etc
CLO 2 Performing different mathematical transformations and histogram PLO 1
C3
based operations for image enhancement and feature extraction
CLO 3 Combining the concepts of image processing with machine learning PLO 2,
to design decision support systems for image processing based PLO 3 C6
applications
CLO 4 Learning the use of Python and OpenCVto implement basic image PLO 5
processing algorithms and to build and execute image processing P2
based projects to solve real life problems
10. Program Learning Outcomes
PLO 1 Engineering Knowledge
An ability to apply knowledge of mathematics, science, engineering fundamentals and an
engineering specialization to the solution of complex engineering problems .
PLO 2 Problem Analysis
An ability to identify, formulate, research literature, and analyze complex engineering problems
reaching substantiated conclusions using first principles of mathematics, natural sciences and
engineering sciences.
PLO 3 Design/Development of Solutions
An ability to design solutions for complex engineering problems and design systems, components or
processes that meet specified needs with appropriate consideration for public health and safety,
cultural, societal, and environmental considerations.
PLO 4 Investigation
An ability to investigate complex engineering problems in a methodical way including literature
survey, design and conduct of experiments, analysis and interpretation of experimental data, and
synthesis of information to derive valid conclusions.
PLO 5 Modern Tool Usage
An ability to create, select and apply appropriate techniques, resources, and modern engineering and
IT tools, including prediction and modeling, to complex engineering activities, with an
understanding of the limitations.
PLO 6 The Engineer and Society
An ability to apply reasoning informed by contextual knowledge to assess societal, health, safety,
legal and cultural issues and the consequent responsibilities relevant to professional engineering
practice and solution to complex engineering problems.
PLO 7 Environment and Sustainability
An ability to understand the impact of professional engineering solutions in societal and
environmental contexts and demonstrate knowledge of and need for sustainable development.
PLO 8 Professional Ethics
Apply ethical principles and commit to professional ethics and responsibilities and norms of
engineering practice.
PLO 9 Individual and Teamwork
An ability to work effectively, as an individual or in a team, on multifaceted and /or
multidisciplinary settings.
PLO 10 Communication
An ability to communicate effectively, orally as well as in writing, on complex engineering
activities with the engineering community and with society at large, such as being able to
comprehend and write effective reports and design documentation, make effective presentations,
and give and receive clear instructions.
PLO 11 Project Management
An ability to demonstrate management skills and apply engineering principles to one’s own work, as
a member and/or leader in a team, to manage projects in a multidisciplinary environment.
PLO 12 Lifelong Learning
An ability to recognize importance of, and pursue lifelong learning in the broader context of
innovation and technological developments