Pattern recognition in grey level images using moment based invariant features

Paschalakis, Stavros and Lee, Peter (1999) Pattern recognition in grey level images using moment based invariant features. In: 7th IEE Conference on Image Processing and its Applications (IPA99), Jul 12-15, 1999, Manchester, England. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1049/cp:19990320

Abstract

Moment based invariants, in various forms, have been widely used over the years as features for recognition in many areas of image analysis. Typical examples include the use of moments for optical character recognition and shape identification. However, most of the work that has been carried out to date using moments and moment invariants is concerned with the identification of distinct shapes using binary images. There can be cases, though, where the different objects to be recognised share identical shapes and binary images fail to convey the necessary information to the recognition processes. The work presented in this paper not only looks at object recognition using binary images, but also addresses the issue of classification among objects which have identical shapes, using grey level images for the moment calculations. Two different moment based feature vectors that provide translation, scale, contrast and rotation invariance are used for the recognition of the different objects. These are the complex moments magnitudes and the Hu (1962) moment invariants. The performance of these two feature vectors are assessed both in the presence and absence of noise and the effect of extending the order of the moments used in their calculations is investigated.

Item Type: Conference or workshop item (Paper)
Additional information: Proceedings Paper; Issue: 465;
Subjects: Q Science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications)
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Depositing User: F.D. Zabet
Date Deposited: 01 May 2009 16:31
Last Modified: 28 May 2014 14:11
Resource URI: http://kar.kent.ac.uk/id/eprint/16441 (The current URI for this page, for reference purposes)
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