Compressive sensing of object-signature

Dan E. Tamir, Natan T. Shaked, Wilhelmus J. Geerts, Shlomi Dolev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Compressive sensing is a new framework for signal acquisition, compression, and processing. Of specific interest are two-dimensional signals such as images where an optical unit performs the acquisition and compression (i.e., compressive sensing or compressive imaging). The signal reconstruction and processing can be done by optical signal processing and/or digital signal processing. In this paper we review the theoretical basis of compressive sensing, present an optical implementation of image acquisition, and introduce a new application of compressive sensing where the actual signals used in the compressive sensing process are image object-signature (an object-signature is a specific representation of an object). We detail the application of compressive sensing to image object-signature and show the potential of compressive sensing to compress the data through analysis of several methods for obtaining signature and evaluation of the rate/distortions results of different compression methods including compressive sensing applied to object-signature.

Original languageEnglish
Title of host publicationOptical Supercomputing - Third International Workshop, OSC 2010, Revised Selected Papers
Number of pages15
StatePublished - 29 Jul 2011
Event3rd International Workshop on Optical Supercomputing, OSC 2010 - Bertinoro, Italy
Duration: 17 Nov 201019 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6748 LNCS


Conference3rd International Workshop on Optical Supercomputing, OSC 2010


  • Compressive Imaging
  • Compressive Sampling
  • Compressive Sensing
  • Digital Signal Processing
  • Optical Super Computing

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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