Orthogonal Image Moment Invariants: Highly Discriminative Features for Pattern Recognition Applications

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Author(s): G.A. Papakostas (Democritus University of Thrace, Greece), E.G. Karakasis (Democritus University of Thrace, Greece)and D.E. Koulouriotis (Democritus University of Thrace, Greece)
Copyright: 2012
Pages: 19
Source title: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies
Source Author(s)/Editor(s): Vijay Kumar Mago (Simon Fraser University, Canada)and Nitin Bhatia (DAV College, India)
DOI: 10.4018/978-1-61350-429-1.ch003

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

This chapter focuses on the usage of image orthogonal moments as discrimination features in pattern recognition applications and discusses their main properties. Initially, the ability of the moments to carry information of an image with minimum redundancy is studied, while their capability to enclose distinctive information that uniquely describes the image’s content is also examined. Along these directions, the computational formulas of the most representative moment families will be defined analytically and the form of the corresponding moment invariants in each case will be derived. Appropriate experiments have taken place in order to investigate the description capabilities of each moment family, by applying them in several benchmark problems.

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