Signal Processing Research Group (SPRG)

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Digital signal processing (DSP) is concerned with the representation of .... Advanced Digital Image Processing, and Computer Vision and Pattern Recognition.
Signal Processing Research Group (SPRG) Introduction: Digital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. The last decade has seen an exponential growth in application of DSP techniques to various fields ranging from biomedical applications to communication and multimedia systems. Digital signal processing is one of the most powerful technologies that will shape science and engineering in the twenty-first century. Revolutionary changes have already been made in a broad range of fields: communications, medical imaging, radar & sonar, high fidelity music reproduction, and oil prospecting, to name just a few. Each of these areas has developed a deep DSP technology, with its own algorithms, mathematics, and specialized techniques. This combination of breadth and depth makes it impossible for any one individual to master all of the DSP technology that has been developed. Digital Signal Processing is distinguished from other areas in computer science by the unique type of data it uses: signals. In most cases, these signals originate as sensory data from the real world: seismic vibrations, visual images, sound waves, etc. DSP is the mathematics, the algorithms, and the techniques used to manipulate these signals after they have been converted into a digital form. This includes a wide variety of goals, such as: enhancement of visual images, recognition and generation of speech, compression of data for storage and transmission, etc.

Figure 1: Radon Transform of an edge image, courtesy http://www.mathworks.com

Although there are a number of tools available for developing algorithms in a simulated environment, MATLAB has evolved as a standard scientific language for the signal processing research community. Figure 1 illustrates Radon transform of a typical edge image using the MATLAB Image Processing Toolbox.

Figure 2: Picture taken from The Scientist and Engineers’ Guide to Digital Signal Processing By Steven W. Smith, Ph.D.

Aims and Objectives At the Signal Processing Research Group (SPRG), we aim to address the most challenging research problems faced by the signal processing community. Cutting edge technologies such as face recognition, watermarking, finger-print recognition and wavelet analysis will be the primary focus of the research.

Why Should I Join the Signal Processing Research Group Postgraduate research, in principle, seeks to make substantial contribution to existing knowledge. Routine technical jobs exploit your skills within the limitations of existing methodologies. Post graduate research however involves innovative exploration of new ideas which would result in novel techniques surpassing the previous benchmarks. You will enjoy the following benefits by pursuing post-graduate study in the engineering sciences: o o

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Working at your own pace under the supervision and guidance of your supervisor. Satisfaction and recognition in being involved with cutting-edge ideas and being responsible for contributing to new knowledge which will lead to the long-term socio-economic benefit of the country. Being awarded a higher degree (e.g. PhD) and title (e.g. Dr) which will define your higher level of standing in the community for the rest of your life. Authoring a dissertation and conference/journal papers which will solidify and permanently establish your contributions to the international research and industry community. Attending conferences where you will "expand your horizons" by interacting and exchanging ideas with fellow students and (inter)national colleagues and experts involved in your area of study. A wider selection of opportunities when you graduate including: pursuing an academic career to further your research, working in highly-paid and very rewarding industry R&D and investing in your ideas and reaping the long-term benefits by patents or directing your own company and consultancy to develop and market your innovative solutions.

Current Projects Digital Watermarking The use of multimedia applications is growing exponentially as the consequence of development of high-speed internet and communication technologies. Digital documents are available to a large number of people in a cost-effective way via the worldwide web. These documents, however, can be easily duplicated. The fact that unlimited copies can be illegally produced is a serious threat to the rights of the content owners. A digital watermark is a chunk of information that is hidden directly in media content, in such a way that it is unnoticeable to a human observer, but easily detected by a computer. Digital watermarking is a popular solution to identify the copyright ownership and track the usage of digital multimedia works.

Figure 3: A typical watermarked image and exaggerated watermark. Courtesy: http://www.willamette.edu/wits/idc/mmcamp/watermarking.htm The aim of this project is to discover novel watermarking schemes for digital image, audio and video contents. Prerequisites: Basic knowledge of signals and systems; Programming in Matlab. Interested students would be expected to take the following postgraduate courses for attaining knowledge and skills related to the project. 1) Advanced DSP, 2) Advanced Digital Image Processing.

Biometrics Biometrics are automated techniques for recognizing or identifying a person based on a physiological or behavioral characteristic. Face, fingerprints, hand geometry, handwriting, iris, vein patterns, and voice are few popular biometric traits for personnel recognition. Biometric technologies are fast becoming the basis of a wide-ranging collection of safe and secure personnel identification solutions. At SPRG, we have planned a multifaceted project involving various aspects of biometric solutions ranging from algorithm development to application of these algorithms using FPGAs (Field Programmable Gate Arrays). In fact, we have been working on three projects using fingerprint and finger vein pattern recognition.

Figure 4: A typical fingerprint impression from http://www.sciencebuzz.org Project 1: Fusing fingerprint and finger vein patterns for personnel identification systems. Prerequisite: Students working on this project would require to have a better understanding of signals and systems. Such students would be expected to take postgraduate courses like Advanced DSP, Advanced Digital Image Processing, and Computer Vision and Pattern Recognition. Project 2: Efficient FPGA implementation of vascular pattern recognition systems. Prerequisite: Students working on this project would require to have basic knowledge of digital systems and image processing and should be familiar with programming in C/C++, Matlab and VHDL. They would be expected to take postgraduate courses like Advanced Digital Image Processing, and Computer Vision and Pattern Recognition.

Project 3: Partial Pattern Matching to reduce the response-time of a personnel identification system Prerequisite: Students working on this project should be well-aware of the basics in algorithms and datastructures and must have good programming skills, preferably in C/C++. They are expected to take the postgraduate level courses of Advanced Digital Image Processing, and Analysis of algorithms. Project 4: Robust Face Recognition Students will work to develop face recognition algorithm robust to illumination, contiguous occlusion, random pixel noise and pose variations.

Figure 5: A typical human face with increasing noise level from left to right. Prerequisite: Students are required to have taken postgraduate course work related to signal processing, image understanding and pattern recognition. Matlab will be used as a tool to develop algorithms.

On the Use of Wavelets for Discriminant Recognition Wavelets have been widely used as an important transformation for pattern classification. It essentially decomposes an image into different frequency subbands. Reported results suggest that some frequencies are more discriminant than others, implying that a careful selection of these discriminant subbands is likely to improve the classification performance. This project aims to develop a generic algorithm for selecting the most discriminant subbands to improve classification. Initial investigations have shown promising results for texture classification.

Team Members Dr. Ghazanfar Monir PhD (Signal Processing, NTU, Singapore) M.Sc (Signal Processing, NTU, Singapore) MCS (Software Engineering, SZABIST, Karachi, Pakistan) BE (Electrical Engineering, NED UET, Pakistan) Associate Professor College of Engineering PAF-KIET Email: [email protected],pk Dr. Imran Naseem PhD (Signal Processing, UWA, Australia) MS (Electrical Engineering, KFUPM, Dhahran, KSA) BE (Electrical Engineering, NED UET, Pakistan) Associate Professor College of Engineering PAF-KIET Email: [email protected]

Publications of Group Members: 1. “Linear Regression for Face Recognition”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), November 2010, Vol. 32 No. 11, pp 2106-2112. 2. “An Information Theoretic Approach to Wavelet Feature Selection for Texture Classification”, Imran Naseem, Duc-Son Pham and Svetha Venkatesh, International Conference on Image Processing 2011, ICIP’11 (Under Review). 3. “Robust Regression for Face Recognition”, Imran Naseem, Roberto Mohammed Bennamoun, Pattern Recognition, 2011 (Accepted). 4.

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"Iterative adaptive spatial filtering for noise-suppression in functional magnetic resonance imaging time-series," S. M. G. Monir and M. Y. Siyal, Journal of imaging systems and technology. (Accepted ).

5. "Denoising functional magnetic resonance imaging time-series using anisotropic spatial averaging," S. M. G. Monir and M. Y. Siyal, Biomedical Signal Processing and Control, vol. 4, pp. 16-25, January 2009. 6. "Adaptive denoising of functional magnetic resonance imaging time series using Wiener filters," S. M. G. Monir and M. Y. Siyal, Journal of imaging systems and technology. (Under Review). 7. “Sparse Representation for Speaker Identification”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, IAPR International Conference on Pattern Recognition (ICPR 2010). 8. “Robust Regression for Face Recognition”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, IAPR International Conference on Pattern Recognition (ICPR 2010). 9. “Sparse Representation for View-Based Face Recognition”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun. Chapter 9, pp 164-177 in book “Advances in Face Image Analysis: Techniques and Technologies”, edited by Prof. Dr. ZHANG YuJin and published by IGI Global Publishers, USA. ISBN-10: 1615209913, EISBN-13: 9781615209927. 10. “Combining Classifiers using the Dempster Shafer Theory of Evidence”, Imran Naseem. Book published by VDM Verlag, ISBN-10 3639232240, ISBN-13 9783639232240. 11. “Sparse Representation for Video-Based Face Recognition”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, book chapter in Advances in Biometrics (Lecture Notes in Computer Science, LNCS series), Springer Berlin / Heidelberg. Volume

5558/2009, pages 219-228. 0302-9743 (Print) 1611-3349 (Online), ISBN 978-3-64201792-6. 12. “Sparse Representation for Video-Based Face Recognition”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, International Conference on Biometrics, ICB’09, Alghero, Italy. 13. “Face Identification using Linear Regression”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun, International Conference on Image Processing ICIP’09, Cairo, Egypt. 14. “User Verification by Combining Speech and Face Biometrics in Video”, Imran Naseem and Ajmal Mian, book chapter in Advances in Visual Computing (Lecture Notes in Computer Science, LNCS series), Springer Berlin / Heidelberg. Volume 5359/2008, pages 482-492. ISSN 0302-9743 (Print) 1611-3349 (Online), ISBN 978-3540-89645-6. 15. “Sparse Representation for Ear Biometrics”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun , book chapter in Advances in Visual Computing (Lecture Notes in Computer Science, LNCS series), Springer Berlin / Heidelberg. Volume 5359/2008, pages 336-345. ISSN 0302-9743 (Print) 1611-3349 (Online), ISBN 978-3540-89645-6. 16. “User Verification by Combining Speech and Face Biometrics in Video”, Imran Naseem and Ajmal Mian in International Symposium on Visual Computing (ISVC), December 1-3, 2008, Las Vegas, Nevada, USA. 17. “Sparse Representation for Ear Biometrics”, Imran Naseem, Roberto Togneri and Mohammed Bennamoun in International Symposium on Visual Computing (ISVC), December 1-3, 2008, Las Vegas, Nevada, USA. 18. "A New Approach to Face Localization in the HSV Space using the Gaussian Model", Mohamed Deriche and Imran Naseem, book chapter in Lecture Notes in Computer Science (LNCS) series, Springer Berlin / Heidelberg. Volume 4678/2007, pages 373383. ISSN 0302-9743 (Print) 1611-3349 (Online), ISBN 978-3-540-74606-5. 19. " A New Approach to Face Localization in the HSV Space using the Gaussian Model”, Mohamed Deriche and Imran Naseem in Advance Concepts for Intelligent Vision Systems (ACIVS), August 28-31, 2007, Delft, the Netherlands. 20. "A New Algorithm for Speaker Identification using the Dempster- Shafer Theory of Evidence", Imran Naseem and Mohamed Deriche, in the 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'06: June 26-29, 2006, LasVegas, USA).

21. “Robust Human Face Detection in Complex Color Images”, Imran Naseem and Mohamed Deriche, in the 12th IEEE International Conference on Image Processing, ICIP’05. 22. “Human Face Detection in Complex Color Images”, Imran Naseem and Mohamed Deriche, in the 3rd IEEE International Conference on Systems, Signals & Devices SSD'05. 23. “A Hybrid PCA-ANN Approach to Face recognition”, Mohamed Deriche and Imran Naseem, in the 2nd IEEE GCC conference 2004. 24. "A region homogeneity approach to overcome the ill-balanced data problem in functional MRI clustering analysis," S. M. G. Monir and M. Y. Siyal, in 2nd International symposium on applied sciences in biomedical and communication technologies, Bratislava, 2009. 25. “Overcoming the ill-balanced data problem in functional MRI clustering analysis”, S. M. G. Monir, M. Y. Siyal, and H. K. Maheshwari, Seventh International Conference on Information, Communications, and Signal Processing, Macau, 2009. 26. “Iterative adaptive filtering for random noise reduction in functional MRI time-series”, S. M. G. Monir, M. Y. Siyal, and H. K. Maheshwari, Seventh International Conference on Information, Communications, and Signal Processing, Macau, 2009. 27. “Noise suppression in functional MRI data using anisotropic spatial averaging”, S. M. G. Monir, M. Y. Siyal, and H. K. Maheshwari, Seventh International Conference on Information, Communications, and Signal Processing, Macau, 2009. 28. “Random Noise Suppression in fMRI time-series Using Modified Spectral Subtraction.” S. M. Monir, M. Y. Siyal, and H. K. Maheshweri, Twelfth IEEE International Multi-topic Conference, Karachi, 2008. 29. “An Adaptive Smoothing Technique for Random Noise Suppression in fMRI Data”, M. Y. Siyal, and S. M. Monir Sixth International Conference on Information, Communications, and Signal Processing, Singapore, 2007. 30. “ A Framework for Interactive Content-Based Image Retrieval,” S. M. G. Monir, and S. K. Hasnain, Ninth IEEE International Multitopic Conference, Karachi, 2005. 31. “Implementation of Viterbi Decoder for WCDMA System,” F. A. Choudhry, and S. M. G. Monir Ninth IEEE International Multitopic Conference, Karachi, 2005. 32. "Performance analysis of Viterbi decoder using a DSP technique," S. K. Hasnain, A. Beg, and S. M. G. Monir, IEEE 8th International Multitopic Conference, Karachi, 2004.