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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): 387-391 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.82

Object Recognition with Lower Order Gegenbauer Moments

Simon Liao and Jing Chen
Abstract—Moment descriptors have been used as the image features in pattern recognition since the early development stage of moment method. The moment method is different from other object feature extraction methods as it captures global properties of an object rather than the local ones. In this research, an effective procedure to select moment features of an object is proposed. By using a set of 6,763 Chinese characters as the testing objects, we will show that the new Gegenbauer moment features perform very well in recognizing all Chinese characters including those that are very close in shapes.

Index Terms—Moment descriptors, gegenbauer moments, object recognition, Chinese character recognition.

Simon Liao and Jing Chen are with the University of Winnipeg, Winnipeg, Manitoba, Canada (e-mail: s.liao@uwinnipeg.ca, je.chen@uwinnipeg.ca).


Cite: Simon Liao and Jing Chen, "Object Recognition with Lower Order Gegenbauer Moments," Lecture Notes on Software Engineering vol. 1, no. 4, pp. 387-391, 2013.

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