M Tech (Computer Science and Applications):Pattern Recognition And Image Processing

Thapar University
In Patiala

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Important information

  • Master
  • Patiala
Description

Important information
Venues

Where and when

Starts Location
On request
Patiala
Thapar University P.O Box 32, 147004, Punjab, India
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Frequent Asked Questions

· Requirements

Admission to M. Tech. (Computer Science and Applications) will be open to a candidate who obtains at least 50% marks in aggregate in the qualifying examination from a recognized university

Course programme

Semester I


Advanced Data Structures
Data Communication and Computer Networks
Computer Organization and Operating Systems
Computational Algorithms in Optimization
Statistical Methods and Algorithms
Database Management and Administration


Semester II

Object Oriented Analysis and Design
Software Engineering
Logic and its applications
Computer Graphics and Multimedia Technologies
Web Technologies and E-Governance


Semester III

Seminar
Thesis (starts)


Semester IV

Thesis (contd.)


Pattern Recognition And Image Processing

Image analysis: Introduction, Imaging systems, Fundamental Steps in Image Processing, Image Transforms- Discrete 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 –DCT and wavelet based techniques.

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

Introduction to pattern recogniton: Features, Feature Vectors and Classifiers, Supervised versus, Unsupervised Pattern Recognition , Bayes Classfier, Linear and Non Linear classifier- Linear Discriminant Functions and Decision Hyperplanes, Support Vector Machine

Feature Selection: Preprocessing, Feature Selection Based on Statistical Hypothesis Testing, The Receiver Operating Characterisitcs (ROC) Curve, Class Separability Measures, Feature Subset selection, Optimal Feature Generation.

Template matching: Similarity Measures Based on Optimal Path Searching Techniques Measures Based on Correlations, Deformable Template Models.

Clustering: Applications of Cluster Analysis, Proximity Measures, Categories of Clustering Algorithms

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, image segmentation and applications using pattern classifiers.


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