What can a bit tell us? Forays into information scalability.
Petros T. BoufounosPrincipal Member of Research Staff
Mitsubishi Electric Research Laboratories
Friday, September 20, 2013|
10:30am - 11:30am
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About the Event
Signal representation theory and practice has focused on how to best represent a signal as efficiently as possible and minimize the distortion on the signal incurred by the representation. However, in many applications the processing stage only requires the extraction of specific information from the signal and the signal itself is not necessarily of interest. In such applications the representation should be information scalable, i.e., adaptable to efficiently represent only the information required by the processing. In this talk, motivated by image-retrieval applications, we demonstrate that such information scalability can be achieved using appropriately designed signal embeddings. Combined with quantization, these embeddings are a perfect fit for inference applications with storage, processing or communication constraints, such as augmented reality. These embeddings capture all or part of the geometry of the signal space, as required for inference, at a very low bit-rate. Thanks to this property we can reduce the storage or transmission rate in image retrieval applications by more than 50%, when compared to existing approaches.
Petros T. Boufounos is a Principal Member of Research Staff at Mitsubishi Electric Research Laboratories (MERL) and a visiting scholar at the Rice University Electrical and Computer Engineering department. Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between September 2006 and December 2008, he was a postdoctoral associate with the Digital Signal Processing Group at Rice University. Dr. Boufounos joined MERL in January 2009. Dr. Boufounos' immediate research focus includes signal acquisition and processing, quantization and data representations, frame theory, and machine learning applied to signal processing. He is also interested into how signal acquisition interacts with other fields that use sensing extensively, such as robotics and mechatronics. Dr. Boufounos is an associate editor at IEEE Signal Processing Letters. He has received the Ernst A. Guillemin Master Thesis Award for his work on DNA sequencing, the Harold E. Hazen Award for Teaching Excellence, both from the MIT EECS department, and has been an MIT Presidential Fellow. He is also a senior member of the IEEE and a member of Sigma Xi, Eta Kappa Nu, and Phi Beta Kappa.
Contact: Anna Gilbert
Sponsor: Anna Gilbert
Open to: Public