HARDWARE

Course Outline:

1. Introduction
In this chapter we give an introduction to Digital Image Processing followed by some examples

• What is Digital Image Processing?
• The Origin of Digital Image Processing
• Examples of fields that use Digital Image Processing
• Fundamental steps in Digital Image Processing
• Components of an Image Processing System

 
2. Digital Image Fundamentals
In this chapter we learn about the fundamentals of Digital Images and the connection to the Visual Perception

• Elements of visual perception
• Light and Electro-Magnetic spectrum
• Image sampling and quantization
• Same basic relationship between pixels

3. Image Enhancement in the Spatial Domain
In this chapter we learn about the Image Enhancement using some Spatial Domain Techniques

• Background
• Some basic gray level Transformations
• Histogram Processing
• Enhancement Arithmetic/Logic operations
• Basics of Spatial filtering
• Smoothing Spatial filtering
• Sharpening Spatial filtering

4. Image Enhancement in the Frequency Domain  
In this chapter we learn about the Image Enhancement using some Frequency Domain Techniques

• Background
• Introduction to the Fourier Transform and the Frequency Domain
  • Smoothing Frequency Domain filters
  • Sharpening Frequency Domain filters
  • Homomorphic filtering
  • Implementation

5. Image Restoration
In this chapter we look at the problem of image degradation and the process of restoration to solve this problem

• A model of the Image Degradation/Restoration process
• Noise models
• Periodic noise reduction by frequency domain filtering
• Linear  Position – Invariant degradation
• Estimation the degradation function
• Inverse filtering
• Constrained Least Squares filtering
• Geometric mean filtering
• Geometric transformations

 6. Multiresolution Processing
In this chapter we look at the mathematics of multiresolution analysis with the use of wavelet Transform

• Background
• Multiresolution Expansion
• Wavelet Transform
• Fast Wavelet Transform
• Wavelet Packet

 7. Image Compression
In this chapter we learn about Image compression techniques

• Fundamentals
• Image compression models
• Elements of Information Theory
• Error-Free compression
• Lossy compression

 8. Image Segmentation
In this chapter we look at techniques for Image Segmentation

• Detection of discontinuities
• Edge Linking and Boundary Detection
• Thresholding
• Region-based segmentation
• Segmentation by Morphological Watersheds
• The use of Motion in segmentation

9. Object Recognition
In this chapter we look the mathematics of multiresolution analysis with the use of wavelet Transform

• Patterns and Pattern classes
• Recognition based on Decision-Theoretic methods
• Neural Networks
• Structural methods

 10. Advanced Topics

• Radon Transform
• Hough Transform
• Machine Vision
• Machine Learning

 11. Summary