Evaluation of image enhancement quality measure in robust PCa for image specularities removal

Edward Chitrahadi, T. Basaruddin

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

Abstract

Recent studies in matrix rank minimization problem has shown that a data matrix can be decomposed into low-rank and sparse matrix convex programming. This pattern of decomposition has wide range of applications, and also known as a form of Robust PCA method. In this paper, we focus on the face recognition application where the objective is to remove specularities which corrupt the images. This paper emphasizes that by using a quantitative image enhancement quality measure, an optimal low-rank approximation is obtained with lower dimensionality, shorter computational time, and comparable approximation quality compared with the approximation performed by the convex program.

Original languageEnglish
Title of host publication2011 5th International Conference on Application of Information and Communication Technologies, AICT 2011
DOIs
Publication statusPublished - 1 Dec 2011
Event2011 5th International Conference on Application of Information and Communication Technologies, AICT 2011 - Baku, Azerbaijan
Duration: 12 Oct 201114 Oct 2011

Publication series

Name2011 5th International Conference on Application of Information and Communication Technologies, AICT 2011

Conference

Conference2011 5th International Conference on Application of Information and Communication Technologies, AICT 2011
Country/TerritoryAzerbaijan
CityBaku
Period12/10/1114/10/11

Keywords

  • Background Variance
  • Convex Optimization
  • Detail Variance
  • Image Enhancement
  • Rank Minimization
  • Robust Principal Component Analysis (Robust PCA)

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