Page: Syllabus of Book of Image Processing and Machine Vision
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Module 1: Digital Image Fundamentals

1.1 Introduction

  • What is Digital Image Processing?
  • Origin of Digital Image Processing
  • Applications & Fields that use Digital Image Processing
  • Fundamental Steps in Digital Image Processing
  • Concepts of an Image Processing System
  • Elements of Visual Perception
  • Image Sensing and Acquisition
  • Image Sampling and Quantization
  • Some Basic Relationships between Pixels
  • Review on Mathematical Tool used in Digital Image Processing
  • Image transformation and Spatial Domain

1.2 Colour Transformation

  • Colour Fundamentals
  • Colour models
  • Pseudocolor Image Processing

Module 2: Image Transforms

2.1 Discrete Fourier Transform

  • 1 D DFT, 2 D DFT
  • Inverse DFT
  • Properties of DFT – Walsh, Hadamard
  • Applications & Uses

2.2 Discrete Cosine Transform

2.3 Haar Transform

Module 3: Image Enhancement

3.1 Some Basic Intensity Transformation Functions

  • Image Negative
  • Log Transformations
  • Power-Law (Gamma) Transformations
  • Piecewise-Linear Transformation Functions

3.2 Histogram Processing

  • Histogram Equalization
  • Histogram Matching (Specification)
  • Local Histogram Processing
  • Using Histogram Statistics for Image Enhancement

3.3 Fundamentals of Spatial Filtering

  • The Mechanics of Spatial Filtering
  • Smoothing Spatial Filters
  • Sharpening Spatial Filters

3.4 Filtering in the Frequency Domain

  • The Basics of Filtering in the Frequency Domain
  • Smoothing and sharpening frequency domain filters
  • Ideal, Butterworth and Gaussian filters,
  • Laplacian
  • Unsharp Masking and Homomorphic filters

Module 4: Morphological & Image Restoration

4.1 Morphology

  • Erosion and Dilation
  • Opening and Closing
  • The Hit-or-Miss Transformation

4.2 Restoration

  • A Model of the Image Degradation/Restoration Process
  • Noise models

Module 5: Patch Antenna

5.1 Point, Line, and Edge Detection

  • Detection of Isolated Points
  • Line detection, edge models
  • Basic and advance edge detection
  • Edge linking and boundary detection
  • Canny's edge detection algorithm

5.2 Thresholding

  • Foundation
  • Role of illumination
  • Basic Global thresholding

5.3 Region Based segmentation

  • Region Growing
  • Region Splitting and merging

5.4 Region Identification

  • Chain code
  • Simple geometric border representation
  • Fourier Transform of boundaries
  • Boundary description using segment sequences
  • B-spline representation

Module 6: Boundary Description & Object Recognition

6.1 Texture

  • Statistical Texture Description Methods - Methods based on spatial frequencies
  • Co-occurrence matrices
  • Edge frequency
  • Primitive length
  • Law‘s texture energy measures

6.2 Object Recognition

  • Knowledge representation
  • Classification Principles
  • Classifier setting
  • Classifier Learning
  • Support vector machine
  • Cluster analysis
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written 10 weeks ago by gravatar for Sanket Shingote Sanket Shingote ♦♦ 250
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