Image Processing

Course Title: Image Processing

Course No: CSC332

Nature of the Course: Theory + Lab

Semester: 5

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

This course covers the investigation, creation and manipulation of digital images by computer. The course consists of theoretical material introducing the mathematics of images and imaging. Topics include representation of two-dimensional data, time and frequency domain representations, filtering and enhancement, the Fourier transform, convolution, interpolation. The student will become familiar with Image Enhancement, Image Restoration, Image Compression, Morphological Image Processing, Image Segmentation, Representation and Description, and Object Recognition.

Course Objectives

The objective of this course is to make students able to: develop a theoretical foundation of Digital Image Processing concepts; provide mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression; gain experience and practical techniques to write programs for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression.

Course Contents

1. Introduction

5 hrs

1.1. Digital Image Fundamentals

Digital Image

A Simple Image Model

Fundamental steps in Image Processing

Elements of Digital Image Processing systems

Element of visual perception

1.2. Image Representation

Sampling and Quantization

Some basic relationships like Neighbors, Connectivity, Distance Measures between pixels

2. Image Enhancement and Filter in Spatial Domain

8 hrs

2.1. Point Operations

Point operations

contrast stretching, clipping and thresholding

digital negative

intensity level slicing

bit plane slicing

Histogram Equalization

2.2. Spatial Operations and Filters

Spatial operations: Averaging, median filtering

spatial low pass and high pass

high boost filter

high frequency emphasis filter

Laplacian filter

magnification by replication and interpolation

3. Image Enhancement in the Frequency Domain

8 hrs

3.1. Fourier Transform

Introduction to Fourier Transform and the frequency Domain

Computing and Visualizing the 2D DFT

Fast Fourier Transform

3.2. Frequency Domain Filters

Smoothing Frequency Domain Filters

Sharpening Frequency Domain Filters

3.3. Other Image Transforms

Other Image Transforms (Hadamard transform, Haar transform and Discrete Cosine transform)

4. Image Restoration and Compression

8 hrs

4.1. Image Restoration

Models for Image degradation and restoration process

Noise Models

Estimation of Noise Parameters

Restoration Filters

Bandrejected Filters, Bandpass Filters

4.2. Image Compression

Image compression models

Pixel coding: run length, bit plane

Predictive and inter-frame coding

5. Introduction to Morphological Image Processing

2 hrs

5.1. Morphological Operations

Logic Operations involving binary images

Dilation and Erosion

Opening and Closing

6. Image Segmentation

8 hrs

6.1. Detection Techniques

Point Detection

Line Detection

Edge Detection

Gradient Operator

6.2. Segmentation Methods

Edge Linking and Boundary Detection

Hough Transform

Thresholding

Region-oriented Segmentation

7. Representations, Description and Recognition

6 hrs

7.1. Descriptors

Introduction to some descriptors (Chain codes, Signatures, Shape Numbers, Fourier Descriptors)

7.2. Pattern Recognition

Patterns and pattern classes

Decision-Theoretic Methods

Overview of Neural Networks in Image Processing

Overview of pattern recognition

Laboratory Works

Students are required to develop programs in related topics using MatLab or suitable programming language.

1.Image Processing Programming

Text Books

1.Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing", Pearson Edition, Latest Edition

Reference Books

1.I. Pitas, "Digital Image Processing Algorithms", Prentice Hall, Latest Edition
2.A. K. Jain, "Fundamental of Digital Image processing", Prentice Hall of India Pvt. Ltd., Latest Edition
3.K. Castlemann, "Digital image processing", Prentice Hall of India Pvt. Ltd., Latest Edition
4.P. Monique and M. Dekker, "Fundamentals of Pattern recognition", Latest Edition

Notes:

This syllabus follows the official B.Sc. CSIT curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.

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