55:148 Digital Image Processing

Course Syllabus

The following sections refer to the numbering used in Sonka-Hlavac-Boyle: Image Processing, Analysis and Machine Vision (3rd edition!).


Chapter 1, Introduction
  • 1.1 Motivation
  • 1.2 Why is Computer Vision Difficult?
  • 1.3 Image Representation and Image Analysis Tasks
Introduction to the MATLAB Image Processing Toolbox
Chapter 2, The Image, its Representations and Properties
  • 2.1 Image representations
  • 2.2 Image digitalization
    • 2.2.1 Sampling
    • 2.2.2 Quantization
  • 2.3 Digital image Properties
    • 2.3.1 Metric and Topological Properties of Digital Images
    • 2.3.2 Histograms
    • 2.3.3 Entropy
    • 2.3.4 Visual Perception of the Image
    • 2.3.5 Image Quality
    • 2.3.6 Noise in Images
  • 2.4 Color (overview)
  • 2.5 Cameras (overview)
Chapter 4, Data Structures for Image Analysis
  • 4.1 Levels of Image Data Representation
  • 4.2 Traditional Image Data Structures
    • 4.2.1 Matrices
    • 4.2.2 Chains
    • 4.2.3 Topological Data Structures
    • 4.2.4 Relational Structures
  • 4.3 Hierarchical Data Structures
    • 4.3.1 Pyramids
    • 4.3.2 Quadtrees
    • 4.3.3 Other Pyramidal Structures
Chapter 5, Image Pre-Processing
  • 5.1 Pixel Brightness Transformations
    • 5.1.1 Position-Dependent Brightness Correction
    • 5.1.2 Gray-Scale Transformation
  • 5.2 Geometric Transformations
    • 5.2.1 Pixel Co-ordinate Transformations
    • 5.2.2 Brightness Interpolation
  • 5.3 Local Pre-Processing
    • 5.3.1 Image Smoothing
    • 5.3.2 Edge Detectors
    • 5.3.3 Zero-Crossings of the Second Derivative
    • 5.3.4 Scale in Image Processing (overview)
    • 5.3.5 Canny Edge Detection (overview)
    • 5.3.8 Local pre-processing in the frequency domain
  • 5.4 Image Restoration
    • 5.4.1 Degradations That are Easy to Restore
    • 5.4.2 Inverse Filtration
Chapter 6, Segmentation I
  • 6.1 Thresholding
    • 6.1.1 Threshold Detection Methods
    • 6.1.2 Optimal Thresholding
  • 6.2 Edge-based Segmentation
    • 6.2.1 Edge Image Thresholding
    • 6.2.2 Edge Relaxation
    • 6.2.3 Border Tracing
    • 6.2.4 Border Detection as Graph Searching
    • 6.2.5 Border Detection as Dynamic Programming
    • 6.2.6 Hough Transform
  • 6.3 Region-based Segmentation
    • 6.3.1 Region Merging
    • 6.3.2 Region Splitting
    • 6.3.3 Splitting and Merging
    • 6.3.4 Watershed Segmentation
    • 6.3.5 Region Growing Post-Processing
  • 6.4 Matching
    • 6.4.1 Matching Criteria
  • 6.5 Evaluation Issues in Segmentation
    • 6.5.1 Supervised Evaluation
    • 6.5.2 Unsupervised Evaluation
Chapter 3, The Image, its Mathematical and Physical Background
  • 3.1 Overview
    • 3.1.1 Linearity
    • 3.1.2 The Dirac Distribution and Convolution
  • 3.2 Linear Integral Transforms
    • 3.2.1 Images as Linear Systems
    • 3.2.2 Introduction to Linear Integral Transforms
    • 3.2.3 1D Fourier Transform
    • 3.2.4 2D Fourier Transform
    • 3.2.5 Sampling and the Shannon Constraint
    • 3.2.6 Discrete Cosine Transform
Chapter 14, Image Data Compression
  • 14.1 Image Data Properties
  • 14.2 Discrete Image Transforms in Image Data Compression

Last Modified: 2008

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