Defence Engineering College

Defence Engineering College

Department of Computer & Information Technology

Course Syllabus

1. Instructor Information

Name

Office Location

Phone Number

E-mail

Office Hours

2. Course Information

Course NameDigital Image Processing & Pattern Recognition

Course CodeCT-4222

Credit Hours3-2-3

Pre-requisiteEL-3112

3. Course Description

Aim: This course is aimed enable the students to understand Image processing and Pattern Recognition tools to process, identify and recognize categorically various images and patterns.

Description:

Introduction-Image-elements of digital image processing-types of images-digitizing characteristics of image digitizer-types of digitizer-image digitizing components-cameras-scanner-film scanning, Digital image display Point operations-algebraic operations-geometric operations. Overview of Fourier transform-FFT Filtering techniques-image restoration, edge detection, image enhancement Pattern recognition-introduction image segmentation-object measurement-classification and estimation

4. Method of Instruction

Class lectures3 lecture hours per week

Active learning (involves the full participation of students)

In-class Tutorial

Study of lecture notes This is fully the responsibility of the learner

DemonstrationsFor each practical session, the concerned sample problem is solved as a demonstration

Lab exercises/exam3 Practical hours per week

A list of problems are issued before each lab session

You are asked to write the programs for the given problems before entering lab

Solutions must be checked and suggested ideas must be provided by the instructor

Viva-voice as part of continuous assessment scheme

Implement mathematical & geometrical calculations

Group Assignment A group may not have more than 4 students

Recognize & evaluate individual contribution

Project

5. Learning Outcomes

After the completion of the course the students will have the following attributes:

5.1 Knowledge

5.1.1 Describe the basic principles of Image Processing

5.1.2 Recognize the concept of digitizing components

5.1.3 define Digital Image Fundamentals

5.1.4 state fundamental Concepts of Digitizing Images

5.1.5 relate usage of Fourier Transform with imaging

5.1.6 identify Image Processing related point, algebraic and geometric operations

5.1.7 state fundamental Concepts of image enhancement and image segmentation

5.2 Intellectual and practical skills

5.2.1Logical thinking to process images

5.2.2Implement image representation techniques

5.2.3Implementation of image sampling and quantization algorithms-

5.2.4Apply mathematical models to process images

5.2.5Write high level programs to process images

5.2.6Apply images processing models in pattern recognition

5.2.7Apply image processing techniques in real life problems

5.3 Attitude and behavior

Appreciate the role of basic digital image processing and pattern recognition concept

6. Course Outline

ChapterWeekTopics to be covered

(Lecture hours)Learning OutcomeAssignments/activity

(Tutorial hours)

Chapter: 1

Introductory concepts1 What are Images?

What is Image Processing

Digital Image Representation

Elements of Digital Image Processing

Terminologies of Digital Image Processing5.1.1You will know introductory concepts about image processing

Chapter: 2

Terminologies of Digital Image Processing

2 Digital Image Fundamentals

Passive and Active imaging

The Human Eye

Structure

Properties of the Human Visual System5.1.2

You will identify the digital image fundamentals

3 Sampling

Spatial Resolution

Sampling Pattern

Quantization

Color

Other Color Model

An Image Model

5.1.3

Chapter: 3

Digitizing Images

4Elements of a Digitizer

Characteristics of Image Digitizer

Types of Image Digitizer

5.1.4

You will identify the elements of digitizer

5 Image Digitizing Components

Light Sources

Light Sensors

Scanning Mechanism

Cameras

Film5.1.4

You will identify image digitizing components

Chapter: 4

The Point, Algebraic and Geometric Operations6 Point Operation

Applications of Point Operations

Linear Point Operation

Algebraic Operation

Introduction

Definitions

Uses of Algebraic Operations

Individual assignment 1

5.1.5

You will identify the point algebraic and geometric operations and can calculate for a given problem

7 Geometric Operation

4.3.1 Introduction

The Spatial Transformation

Gray-Level Interpolation

Implementation

Applications of Geometric Operations5.1.4

Exercise on the use of geometric operation and gray-level interpolation

8 Fourier Transformation

Fourier Theory

Basic Concept

Extension to Two Dimensions

The Fast Fourier Transform

Exercise on mathematical foundations of Fourier transforms

Wk 9

Chapter: 5Image Enhancement and Restoration

10 Background

Intensity Transformation Functions

Histogram Processing

Generating and Plotting Image Histograms

Histogram Equalization5.1.5

You will use appropriate enhancement in a given image appropriately by identifying the requirement of a problem.

111.1. Spatial Filtering

1.2. Image Smoothing

1.3. Image Sharpening

Image Restoration5.1.6

Perform image filtering using MATLAB

Chapter: 6 Image Segmentation

12 Point, Line and Edge Detection

Point Detection

Line Detection

Edge Detection using Function edge

Sobel Edge Detector

Prewitt Edge Detector5.1.6

You will solve the given problem using appropriate

Edge detection methods and process it.

13 Roberts Edge Detector

Laplacian of a Gaussian (LoG) Detector

Zero-Crossing Detector

Canny Edge Detector

Thresholding

Region-Based Segmentation

Individual assignment 2

5.1.6

Chapter: 7Basic Concept of Pattern Recognition

14 Pattern and Classification as Knowledge

What are the Pattern Recognition and Classification Problems?5.1.7You will use the basic concepts of pattern recognition to detect pattern of an image

15 Recognition Vs Classification

Recognition Process

Approaches to Recognition and Classification

Clustering

Chapter: 8

Structural Pattern Recognition 16 Structure in Objects

Alphabets and Strings

Language and Grammars5.1.7

You will identify structural pattern recognition in your given problem.

17 Syntactic Pattern Recognition

Automata and Recognizers

Stochastic Automata

Generating Grammars for Classes: Grammatical Inference

5.1.7

Wk 18Final Examination period

7. Laboratory Activities

No 1

2

3

4

5.

6

7

8. Required Text and Reference

Text Book( Rafael C. Gonzalez and Richard E., Digital Image Processing, 2nd Edition, Woods-Pearson Education Inc, 2005( Carl G Looney, Pattern Recognition Using Neural Networks, Oxford University Press, 1997

Reference Books1.Rafael C. Gonzalez and Richard E., Digital Image Processing Using MATLAB, Wesley Publication, 1993

2. Anil K. Jain, Fundamentals of Digital Image Processing, 2nd ed. Pearson Edu.Inc., 2004

3. Kenneth R Castleman, Digital Image Processing, Prentice Hall Inc.New Jercy, 1996

Software

9. Assessment

TypeWeightDue dateBehavior and Criteria

Mid semester Exam30%9th week of the semesterExamination will be set to address learning outcomes 5.1.1, 5.1.2, 5.1.3, 5.1.4

The criteria is to get all questions answered correctly

Final semester Exam50%18th week of the semesterExamination will be set to address learning outcomes

5.1.5,5.1.6, 5.1.7 and some questions before mid .

the criteria is to get all questions answered correctly

Group Assignment10%Group Assignments will be prepared by corresponding instructor and approved by the departmentGroupWise unique problems will be given and assessed.

Individual assignment 10%6th and 13th week of the semesterIndividually problems will be given and assessed

Project

10. Academic Honesty

Copying from any outside sources (e.g. Fellow students, and Internet, etc.) on any material to be graded is not permitted, and will be considered cheating. Cheating will result in failure of the assignment, failure of the class and/or face possible disciplinary action. Each of You is responsible for securing his or her work from copying. Each of You is expected to abide by college policies on academic conduct.

11. Due Date

All assignments must be turned in the class on the due date for full credit. No assignment will be accepted after class on the due date. Failure of submission and presentation of the group assignment will be awarded as zero out of 10 points.

12. Classroom Behavior

Anything that disturbs your instructor or your colleagues during the class period is considered a troublesome behavior. Examples include: Using mobiles, making offensive remarks, sleeping, working on assignments related to other courses, etc. troublesome behaviors are completely prohibited.

13. Approval (Affidavit)

NameSignatureDate

Instructor:

Section Head:

Department Head:

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