Course
information
C++ for Image Processing
(DCS339 / AMCM053)
Aims
The main purpose of this course is to provide an introduction to basic knowledge for C++ programming and basic concepts and methodologies for digital image processing.
Prerequisites
Some mathematical background, such as calculus, complex arithmetic, statistics, linear algebra, basic understanding of signal processing (Fourier transform), some programming experience.
Objectives
- Be able to use effectivly the C++ programming language
- Be able to implement low level image processing algorithms.
- Understand image file formats
- Implement contrast enhancement by histogram manipulation
- Know frequency domain transform methods
- Use filtering algorithms for image smoothing and sharpening
- Ability to undertake independent advanced scholarship
- Ability to interpret/ conceptualise/ independently evaluate techniques and applications of techniques in this field of study
- An awareness of the wider context and critical issues surrounding this field of study
Description
This course gives students a practical introduction to C++ and uses this programming language to examine applications in low level image processing. Areas covered include image representation examining perception, sampling and display, and image transforms and image enhancement using point and spatial operations. Also considered are image processing methods such as convolution, frequency filtering and image restoration, compression and segmentation.
Assessment
One 2 hour and 30 minute examination, 80%. 20% assessed coursework
Reading
Textbooks:
Digital Image Processing (2nd Edition), by Rafael C. Gonzalez, and Richard E. Woods, 793 pages, Prentice Hall, 2002, ISBN: 0201180758.
C++: The Complete Reference, (4th Edition), by Herbert Schildt, 1056 pages, McGraw-Hill Osborne Media, 2002, ISBN: 0072226803
Reference books:
Digital Image Processing using MATLAB, by Rafael C. Gonzalez, and Richard E. Woods, 793 pages, Prentice Hall, 2002, ISBN: 0201180758.
Image Processing: The Fundamentals, by M. Petrou and P. Bosdogianni, 354 pages, John Wiley & Sons, 1999, ISBN: 0471998834
Algorithms for Image Processing and Computer Vision, by J. R. Parker, 432 pages (with CD-ROM), John Wiley & Sons, 1996, ISBN: 0471140562.
Computer Vision and Image Processing, by Tim Morris, 320 pages, Palgrave Macmillan, 2003, ISBN: 0333994515
Reference journals:
IEEE Trans. Image Processing
IEEE Trans. Signal Processing
IEEE Trans. Medical Imaging
Computer Vision and Image Understanding (CVIU)
Graphical Modeling and Image Processing
Computer Vision, Graphics and Image Processing (CVGIP)
Reference conference proceedings:
International Conference on Image Processing (ICIP)
IEEE International Conference on Computer Vision (ICCV)
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
Webpages and Mailing Lists
The Course
- : http://www.dcs.qmul.ac.uk/~phao/CIP/
The Course - Image Processing labs: http://www.dcs.qmul.ac.uk/~phao/CIP/Labs/
The Course
- Lecture notes (image processing): http://www.dcs.qmul.ac.uk/~phao/CIP/Notes/
The Course
- Coursework: http://www.dcs.qmul.ac.uk/~phao/CIP/Labs/cwk/
The Course
- Intranet: https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS339/
The Course
- Mailing lists: course-DCS339@dcs.qmul.ac.uk,
course-AMCM053@dcs.qmul.ac.uk
Past exam papers are available on the DCS intranet or the QMUL library.
The text book and image processing: http://www.imageprocessingbook.com
C++ language tutorial: http://www.cplusplus.com/doc/tutorial/index.html
C++ library reference: http://www.cplusplus.com/ref/
MSDN to the C++ reference: http://msdn.microsoft.com/library/default.asp?url=/library/en-us/vclang/html/vcrefCPlusPlusLanguageReference.asp
Teaching
Image processing: Pengwei Hao (phao@eecs.qmul.ac.uk)
Labs and exercises: Haibo Mei(haibo.mei@eecs.qmul.ac.uk), Yixian Liu (yixianliu@eecs.qmul.ac.uk), and Eduardo Peixoto (eduardo.fernandes@eecs.qmul.ac.uk).
Schedule (tentative)
|
Teaching (Thursdays) |
Lectures (10am-12noon) |
DIP & C++ Reading Sections |
Lab Session (Mondays) |
|
Week 1 |
Introduction C++: Classes, Objects, Inheritance, Protection |
Chapter 1, C++ |
Week 2 |
|
Week 2 |
C++: Pointers, Memory Management and Arrays |
C++ |
Week 3 |
|
Week 3 |
C++ I/O: STL, File I/O, Namespaces, Preprocessing |
C++ |
Week 4 |
|
Week 4 |
Image representation, sampling and quantization; pixel relationships, distance, image operations, averaging |
Chapter 2 |
Week 5 |
|
Week 5 |
Image filtering/convolution, smoothing and sharpening |
3.5, 3.6 |
Week 6 |
|
Week 6 |
Image enhancement in spatial domain, histogram |
Chapter 3 |
Week 7 |
|
Week 7 |
Reading week |
Week 8 |
|
|
Week 8 |
Fourier transform, Image enhancement in frequency domain |
Chapter 4 |
Week 9 |
|
Week 9 |
Image restoration: degradation models, inverse filters, deconvolution |
Chapter 5 |
Week 10 |
|
Week 10 |
Image segmentation: edge detection, thresholding |
Chapter 10 |
Week 11 |
|
Week 11 |
Image compression: information theory, Huffman coding, run-length coding, lossy compression, compression standards |
Chapter 8 |
Week 12 |
|
Week 12 |
Revision (exercises) |
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Coursework
http://www.dcs.qmul.ac.uk/~phao/CIP/Labs/cwk/
Test Images
Baboon Lena Peppers
Barbara Goldhill Cameraman
Baboon Lena Peppers
These images (BMP format) can be downloaded here: http://www.dcs.qmul.ac.uk/~phao/CIP/Images