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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2013 Vol.1(4): 356-359 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.76

Local Feature Extraction Property of Krawtchouk Moment

Bing Hu and Simon Liao
Abstract—Moment descriptors have long been applied in object recognition since the early years of the development of the moment theories. In recent years, the emergence of discrete orthogonal moments has substantially enriched the moment methods. In this research, we have focused our study on the local feature extraction of Krawtchouk moment and the relationship between the Region of Interest (ROI) of an image and the parameter, p, of the Krawtchouk polynomials. It has been observed that by changing parameter p of the Krawtchouk moment, the pair of reconstructed images from different orders of Krawtchouk moments with the complimentary parameters, p and 1-p, are centrosymmetric.

Index Terms—Discrete orthogonal moments, Krawtchouk moments, Region of Interest (ROI), centrosymmetric.

The authors are with the Applied Computer Science Department , University of Winnipeg, Manitoba, Canada (e-mail: hu-b86@webmail.uwinnipeg.ca, s.liao@.uwinnipeg.ca).


Cite: Bing Hu and Simon Liao, "Local Feature Extraction Property of Krawtchouk Moment," Lecture Notes on Software Engineering vol. 1, no. 4, pp. 356-359, 2013.

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