G52IIP Introduction to Image Processing Course Web Page
2011/12 Autumn Semester
Digital image processing is a key enabling technology of today's digital society, and is a fundamental building block of digital photography, digital television, computer games, computer vision, and computer graphics. This module gives an introduction to the field of digital image processing.
The course will make use of lecture slides and notes, texbooks, and online learning resourse
There are many texbooks on this subject, here is a classic one
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2008 (UoN Library Copies)
Useful online resourses (amongst many others) include
There are many free image processing tools. ImageJ is a public domain Java image processing program and was designed with an open architecture that provides extensibility via Java plugins. Users can develop acquisition, analysis and processing plugins using ImageJ's built in editor and Java compiler. If you have done Java programming in your first year, you may find this tool useful.
However, this course is much more than just using image processing tools, it is about gaining in-depth understanding of the theory and algorithms behind the tools. Therefore, you are expected to be able to put theory into practice through implementing various image processing tasks using computer programs (you can use your favourite programming languages).
We will achieve this through a variety of activities – lectures, lab sessions, guided- and self-study, using a variety of learning resources, lecture slides/notes, lab practical guides, textbooks, and online materials.
Timetable Lectures: Thursday 9:00- 11:00 JC-EXCHGE-B.LT3 || Labs: Friday 10:00 - 11:00 JC-COMPSCI-A32
Coursework (25%): Out 24th October 2011 || Deadline 4:00PM 30th November 2011
Slides and Handouts
I have divided my slides into distinctive topics, sometimes these will be presented within one lecture, on other occasions they will be spread across multiple lectures. Note that some slides published before a lecture may contain only an outline of the lecture's topics, many gaps will be filled in class.
Topic 1: Introduction and overview of the course
Reading: Textbook, Chapter 1
Lab 1 Sample Matlab Code
Topic 2: Digital image fundamentals
A simple image model, sampling and quantization, color imaging
Slides ClassSlides SummarySlides
Reading: Chapter 2 of the textbook. Online resources: Color image
Lab 2 Sample Matlab Code
Topic 3: Image processing theory and practice
Intensity transforms and spatial filtering
Reading: Chapter 3 of the textbook (Chapter 4 in 2nd edition)
Online resources: Point operations, digital filters, bilateral filtering, image transformation and filtering
Compute Gaussian Filtering Masks using Excel, Example 1 (variance = 1 pixel); Example 2 (variance = 0.5 pixel)
Lab 3 Sample Matlab Code
Image transforms and filtering in the frequency domain
Slides ClassSlides 2011LectureSlides
Reading: Chapter 4 of the textbook (chapter 3 in 2nd edition)
Online resources: Fourier transform theory, properties, another link, discrete Fourier transform(DFT), 2D Fourier transform, frequency domain filtering
Lab 4 Sample Matlab Code
Topic 4: Image compression
Reading: Chapter 8 of the textbook (3rd edition, or corresponding chapter in other editions).
Online resources: jpeg, mpeg
Topic 5: Edge detection and segmentation
Reading: Chapter 10 of the textbook (3rd edition, or corresponding chapter in other editions).
Online resources: Corner and interesting point detector(CVonline), edge detection (CVonline), region segmentation (CVonline), feature detector (HIPR), image analysis (HIPR)
Topic 6: Selected advanced topics
Reading: Content-Based Image Retrieval at the End of the Early Years
08/09 G52IVG 10/11 G52IIP