Masters of Computer Applications:Image Processing and Computer Vision

Semester I

Discrete Mathematical Structures
Elements of Electronics Engineering
Problem Solving and Programming in C
Operating Systems
Computer Organization and Architecture


Semester II

Data Structures
Fundamental of Microprocessors and Interfacing
Object Oriented Programming using c++ and Java
System Analysis and Design
Statistics and Combinatorics


Semester III

Software Engineering
Data Base Management System
Design and Analysis of Algorithms
Operations Research
Computer Networks


Semester IV

Advanced Java and Network Programming
Computer Graphics and Multimedia
ERP and Tools
Minor Project


Semester V

Information and Network Security
Software Project Management
Net Framework and C# Programming


Semester VI

System Development Project


Image Processing and Computer Vision

Introduction: Image analysis and computer vision, Imaging systems, Fundamental Steps in Image Processing, Elements of Digital image processing systems, Sampling and quantization, some basic relationships like neighbours, connectivity, Distance measure between pixels, Imaging Geometry.

Image Transforms: Discrete Fourier Transform, Some properties of the two-dimensional Fourier transform, Fast Fourier transform, Inverse FFT, Wavelet transforms.

Image Enhancement: Spatial domain methods, Frequency domain methods, Enhancement by point processing, Spatial filtering, Lowpass filtering, Highpass filtering, Homomorphic filtering, Colour Image Processing.

Image Restoration: Degradation model, Diagnolization of Circulant and Block-Circulant Matrices, Algebraic Approach to Restoration, Inverse filtering, Wiener filter, Constrained Least Square Restoration, Interactive Restoration, Restoration in Spatial Domain.

Image Compression: Coding, Interpixel and Psychovisual Redundancy, Image Compression models, Error free comparison, Lossy compression, Image compression using wavelets, Image compression standards.

Image Segmentation: Detection of Discontinuities, Edge linking and boundary detection, Thresholding, Region Oriented Segmentation, Motion based segmentation.

Representation and Description: Representation schemes like chain coding, Polygonal Approximation, Signatures, Boundary Segments, Skeleton of region, Boundary description, Regional descriptors, Morphology.

Recognition and Interpretation: Elements of Image Analysis, Pattern and Pattern Classes, Decision-Theoretic Methods, Structural Methods, Interpretation.

Laboratory Work: The lab work will be based on operations on images. The programs will be based on image enhancement, image zooming, image cropping, image restoration, image compression and image segmentation etc.