Announcements

  • Welcome to this module. I hope that you will enjoy the contents and find it useful.
  • SCREENCASTS: Screencasts 1, 2 and 3 are available under the tab SCHEDULE.
  • The (suggested) weekly schedule of this module is available here.



Introduction

This module is an introduction to digital image processing. The techniques that will be covered are: intensity transformation, image enhancement (both in the spacial and in the frequency domain), image restoration, color models, and morphological image filters.

The mathematical aspects of Fourier analysis will also be revised before frequency based image enhancement is done.

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Handbook

The handbook is

Digitial Image Processing, 3rd Edition R.C. Gonzalez & R.E. Woods, Pearson Publishing.

The second edition or the newer fourth edition will also be fine (page numbers may differ, but otherwise the contents are essentially the same).

You may buy a copy (the fourth edition also has a more affordable digital version available) at

https://www.pearson.com/us/higher-education/program/Gonzalez-Digital-Image-Processing-4th-Edition/PGM241219.html?

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Assessment


This module is evaluated according to the flexible evaluation model.

There are no tests.

You will be required to do six assignments and hand them in. The marks obtained for these assignments will contribute 100% to your final pass mark. Assignments will be weighted differently (according to the volume of work contained in the assignment). The assignments on Color processing and Restoration count 10% each. The other four assignments count 20% each.

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Contents


The table below lists the sections from Gonzalez and Woods (Edition 3) that will be covered. However, for your own background, you should also read Chapters 1 and 2.

Content (Section numbers in ZILL)
  • 3.1 Background (to point transformations)
  • 3.2 Intensity transformations
  • 3.3 Histogram equalization
  • 3.4 Spatial filtering fundamentals
  • 3.5 Smoothing masks
  • 3.6 Sharpening masks
  • 4.1 - 4.6 will be replaced by NOTES ON FOURIER ANALYSIS
  • 4.7 Filtering in the frequency domain
  • 4.8 Image smoothing (Ideal, Butterworth, Gaussian filters)
  • 4.9 Image sharpening
  • 4.10 Selective filtering
  • 5.1 Model of image degradation
  • 5.2 Noise models
  • 5.3 Restoration of images with noise - Spatial filtering
  • 5.8 The Wiener filter
  • 6.1 Color fundamentals
  • 6.2 Color models
  • 6.7 Color segmentation
  • 9.1 Introduction to morphological filtering
  • 9.2 Erosion and dilation
  • 9.3 Opening and closing
  • 9.4 The hit-or-miss transform
  • 9.5 Morphological algorithms
  • 9.6 Gray scale morphological filtering
  • 10.2 Point, Line and Edge detection (especially Canny)
  • 10.3 Thresholding
  • 11.2.3 Fourier descriptors

A suggested schedule of how you should pace yourself is available here.

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What you must do

This module requires complete self study. It is required of you to read through the relevant sections in GONZALES & WOODS, and to make sure you understand it. There are problems at the end of each chapter. I will in due course suggest some problems for you to do. (It is unnecessary to attempt to do all the problems - that is probably too much.)

I will also post some screen-casts on this web site highlighting the most important aspects of each chapter or explaining some of the more difficult concepts. Please watch these screen-casts as they become available.

Do the assignments and submit them on the SunLearn site by the due date. The first assignment is available. I am still reconsidering the content of the other assignments, and will make them available in due course. (The titles of the assignments are visible, so that you can have some idea of what is to follow.)

You are invited to ask questions, either by email, or on the forum on the SunLearn site. I will answer them in the best way I can.



Assignments


The assignments will consist of image related problems that must be solved by programming algorithms and/or procedures. You may use any programming language of your choice, though the two platforms that have been used mostly by past students were MATLAB and PYTHON. (I will use only MATLAB in all demonstrations.)

Each assignment must be handed in as a printable report that discusses the problem and its solution and that shows the results (as figures) inside the report. It must use full sentences and good linguistic style. The code that was used to generate the solution, must also be appended.

The list of assignments and their due dates are here.

A submission point will be created on SunLearn for submission of assignments.

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Additional Notes


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Download


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.

(2020)


Handbook

Gonzales and Woods, 3d Ed.

Digitial Image Processing, 3rd Edition
R.C. Gonzalez & R.E. Woods,
Pearson Publishing (2010)


Lecturer


Dr MF Maritz

  Dr Milton Maritz
  Engineering Building A416
  mfmaritz 'at' sun.ac.za
  021-851-6136 (home)
  082-8784878 (cell)
  [The home number is
    preferred.]