Name of Subject  : IMAGE PROCESSING  (8 CS 4.2)

Unit

Contents

Introduction and Fundamentals: Motivation and Perspective, Applications, Components of Image Processing

System, Element of Visual Perception, A Simple Image Model, Sampling and Quantization. Image Enhancement in

Spatial Domain: Introduction; Basic Gray Level Functions – Piecewise-Linear Transformation Functions: Contrast

I

Stretching; Histogram Specification; Histogram Equalization; Local Enhancement; Enhancement using

Arithmetic/Logic Operations – Image Subtraction, Image Averaging; Basics of Spatial Filtering; Smoothing - Mean

filter, Ordered Statistic Filter; Sharpening – The Laplacian.

Image Enhancement in Frequency Domain: Fourier Transform and the Frequency Domain, Basis of Filtering in

Frequency Domain, Filters – Low-pass, High-pass; Correspondence Between Filtering in Spatial and Frequency

Domain; Smoothing Frequency Domain Filters – Gaussian Low pass Filters; Sharpening Frequency Domain Filters

– Gaussian High pass Filters; Homomorphic Filtering.

II

Image Restoration: A Model of Restoration Process, Noise Models, Restoration in the presence of Noise only

Spatial Filtering – Mean Filters: Arithmetic Mean filter, Geometric Mean Filter, Order Statistic Filters – Median Filter,

Max and Min filters; Periodic Noise Reduction by Frequency Domain Filtering – Band pass Filters; Minimum Mean-

square Error Restoration.

Color Image Processing: Color Fundamentals, Color Models, Converting Colors to different models, Color

Transformation, Smoothing and Sharpening, Color Segmentation.

III

Morphological Image Processing: Introduction, Logic Operations involving Binary Images, Dilation and Erosion,

Opening and Closing, Morphological Algorithms – Boundary Extraction, Region Filling, Extraction of Connected

Components, Convex Hull, Thinning, Thickening.

Registration: Introduction, Geometric Transformation – Plane to Plane transformation, Mapping, Stereo Imaging –

Algorithms to Establish Correspondence, Algorithms to Recover Depth.

Segmentation: Introduction, Region Extraction, Pixel-Based Approach, Multi-level Thresholding, Local

IV

Thresholding, Region-based Approach, Edge and Line Detection: Edge Detection, Edge Operators, Pattern Fitting

Approach, Edge Linking and Edge Following, Edge Elements Extraction by Thresholding, Edge Detector

Performance, Line Detection, Corner Detection.

Feature Extraction: Representation, Topological Attributes, Geometric Attributes. Description: Boundary-based

V

Description, Region-based Description, Relationship. Object Recognition: Deterministic Methods, Clustering,