Course information for Image Processing Course information ECS605U/ECS776P - Image Processing Aims The main purpose of this course is to provide an introduction to the knowledge 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 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 an introduction to digital image processing and uses a programming language Java to implement simple applications in low level image processing. Areas covered include image representation, image 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 examination for 80% and assessed coursework for 20% Reading Textbook: Digital Image Processing (Global Edition, 4th Edition), by Rafael C. Gonzalez, and Richard E. Woods, 1024 pages, Pearson, 2017, ISBN13: 9781292223049, ISBN10: 1292223049. Reference book: Digital Image Processing Using MATLAB, by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, 827 pages, Gatesmark Publishing; 2nd edition (2009), ISBN-10: 0982085400, ISBN-13: 978-0982085400. 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: British Machine Vision Conference (BMVC) 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.eecs.qmul.ac.uk/~phao/IP/ The Course - QM+ : http://qmplus.qmul.ac.uk/course/view.php?id=3252 The Course - Image Processing labs: http://www.eecs.qmul.ac.uk/~phao/IP/Labs/ The Course - Lecture notes (image processing): http://www.eecs.qmul.ac.uk/~phao/IP/Notes/ The Course - Coursework: http://www.eecs.qmul.ac.uk/~phao/IP/Labs/cwk/ The Course - Mailing lists: ECS605U@lists.eecs.qmul.ac.uk; ECS776P@lists.eecs.qmul.ac.uk Test images (RAW, BMP, and TIF formats): http://www.eecs.qmul.ac.uk/~phao/IP/Images The text book website: http://www.imageprocessingplace.com/ Past exam papers are available on QM+. Teaching Image processing: Pengwei Hao (p.hao@qmul.ac.uk) Labs and exercises: Bingqing Guo (b.guo@qmul.ac.uk), Xindi Zhang (xindi.zhang@qmul.ac.uk) . Schedule (tentative) Teaching Lectures DIP Reading Sections Lab Session Week 1 Introduction Chapter 1 No Lab Session Week 2 Image representation, sampling and quantization Chapter 2 Session 1 Week 3 Point processing for image enhancement Chapter 3 Session 2 Week 4 image histogram Chapter 3 Session 3 Week 5 Image filtering/convolution, smoothing and sharpening Chapter 3 Session 4 Week 6 Fourier transform Chapter 4 Session 5 Week 7 Review and Feedback Week Session 6 Week 8 Image enhancement in frequency domain Chapter 4 Session 7 Week 9 Image restoration: degradation models, Chapter 5 Session 8 Week 10 Image segmentation: edge detection, thresholding Chapter 10 Session 9 Week 11 Image compression: information theory, Huffman coding, run-length coding, lossy compression, compression standards Chapter 8 Coursework Assessment Week 12 Revision (exercises) --- Coursework Assessment
