Fast GPU implementation of large scale dictionary and sparse representation based vision problems

Pradeep Nagesh, Rahul Gowda, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recently, Computer Vision problems like Face Recognition and Super-Resolution solved using sparse representation based methods with large dictionaries have shown state-of-the-art results. However such methods are computationally prohibitive for typical CPUs, especially for a large dictionary size. We present fast implementation of these methods by exploiting the massively parallel processing capabilities of a GPU within a CUDA framework, owing to its easy off-the-shelf availability and programmer friendliness. We provide details of system level design, memory management and implementation strategies. Further, we integrate the solution to the preferred scientific computational platform - MATLAB.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages1570-1573
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • CUDA
  • Face recognition
  • GPU-based computing
  • Sparse representation
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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