Image Processing and Pattern Recognizition

Course Title: Image Processing and Pattern Recognizition
Full Marks: 45 + 30
Course No: C.Sc. 623
Pass Marks: 22.5+15
Nature of the Course: Theory + Lab
Credit Hrs: 3

Course objectives:

To be familiar with processing of the digital images, recognition of the pattern and their applications.

Unit 1: Introduction (6 Hrs)

Digital Image, Fundamental steps in Image Processing, Elements of Digital Image Processing systems, Element of visual perception. Digital Image Fundamentals: A Simple Image Model, Sampling and Quantization, Some Basic Relationships between Pixels, Image Geometry transforms in 2D.

Unit 2: Image Enhancement and Filtering (14 Hrs)

Image Enhancement in the Spatial Domain: Introduction to Spatial and Frequency Methods, Basic Gray Level Transformations, Histogram Equalization, Histogram Matching, Histogram Processing, Local Enhancement, Image Subtraction, Image Averaging, Basics of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters. Fourier Transforms: Introduction to Fourier Transformation, Discrete Fourier Transformation, Fast Fourier Transformation, Fourier Properties, 2D FT, Inverse Fourier Transform. Filtering in the Frequency Domain: Filtering in the Frequency Domain, Correspondence between Filtering in Spatial and Frequency Domain, Smoothing Frequency Domain Filters, Sharpening Frequency Domain Filters.

Unit 3: Image Restoration and Compression (10 Hrs)

Image Restoration: Models for Image degradation and restoration process, Noise Models, Estimation of Noise Parameters, Restoration Filters, Bandrejected Filters, Bandpass Filters, Inverse Filtering, Wiener Filtering. Image Compression: Image compression models, standards and coding Techniques.

Unit 4: Image Segmentation and Representation (8 Hrs)

Image Segmentation: Point Detection, Line Detection, Edge Detection, Gradient Operator, Edge Linking and Boundary Detection, Hough Transform, Thresholding, Region‐oriented Segmentation. Representation: Chain Codes, Polygonal Approximations, Signatures, Boundary Segments, Skeleton of a Region, Boundary Descriptors, Shape Numbers, Fourier Descriptors, Regional Descriptors, Simple Descriptors, Topological Descriptors.

Unit 5: Pattern Recognition (7 Hrs)

Introduction to Pattern Recognition, Patterns and Pattern Classes, strategies and models, Pattern Classifiers, Neural Network and Neural learning tools for pattern recognition, Structural Methods.

Laboratory Work:

Developing programs of above features using C/C++/MATLAB.

Text Book:

  • Rafel C. Gonzalez and Richard E. Woods, “Digital Image Processing“, 3rd Edition, Pearson education

Reference Books:

  • W. K. Pratt, “Digital Image Processing”, 3rd Edition, John Wiley and Sons, New York.