LATEST NEWS
Conference Proceedings from the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) are available for the group under resources.
Conference Proceedings from the 2012 IEEE International Conference on Image Processing (ICIP 2012 ) are available for the group under resources.
Haiping, Lu, an alumini of the dsp group, has received the 2013 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award.
Thesis templates recommended by School of Graduate Studies are uploaded under the Resources directory, both in Microsoft Word format and in latex format (zip folder). More thesis formatting information can be found on the SGS website here.
Welcome to the Multimedia Laboratory
The Multimedia Laboratory at the University of Toronto, is part of the Communications Group at the Edward S. Rogers Sr. Department of Electrical and Computer Engineering. Our Laboratory has been at the forefront of the signal and image processing field. Specifically, research has been focused in the areas of biometric systems, secure and privacy enhancing multimedia solutions, nonlinear signal and image processing, multichannel image processing, morphological filters, neural networks and image and video coding.
Professor K. N. Plataniotis, Multimedia Lab Director
Project Highlights
Wireless Multi Input Multi Output (MIMO) Channel Simulation Package
The project aimed to provide a MATLAB software package for simulating wireless MIMO
communication channels under practical assumptions. The final software provides an
accurate simulation test bed which incorporates the effect of various pulse-shapes,
fading models, noise structures, channel estimators, synchronization strategies, etc.
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Learning for Biometric Signal Recognition
Biometrics is an important component in security-related applications such as access control, forensic investigation, and identity fraud protection. We focused on the very important problem of feature extraction through subspace learning in biometric systems.
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Privacy Protected Surveillance Using Secure Visual Object Coding
The Secure Shape and Texture Set Partitioning in Hierarchical Trees (SecST-SPIHT) secure visual object coder allows individual, arbitrarily shaped objects to be efficiently coded (compressed) and encrypted. We use this in surveillance applications to protect the privacy of individuals appearing in the surveillance footage.
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Multilinear Subspace Learning (MSL)
This project aims to provide an overview of resources concerned with theories and applications of multilinear subspace learning (MSL). The origin of MSL traces back to multi-way analysis in the 1960s and they have been studied extensively in face and gait recognition. With more connections revealed and analogies drawn between multilinear algorithms and their linear counterparts, MSL has become an exciting area to explore for applications involving large-scale multidimensional (tensorial) data as well as a challenging problem for machine learning researchers to tackle.
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