Pattern recognition in grey level images using moment based invariant features

• Published in: 7th International Conference on Image Processing and its Applications pp. 245 - 249
• Main Authors: Paschalakis, S, Lee, P
• Format: Conference Proceeding
• Language: English
• Published: London IEE 1999
• Subjects: Applied sciences; Exact sciences and technology; Image recognition; Information, signal and communications theory; Pattern recognition; Signal processing; Telecommunications and information theory; object recognition; moment based feature vectors; contrast invariance; image classification; moment based invariant features; object classification; translation invariance; scale invariance; complex moments magnitudes; Hu moment invariants; image analysis; grey level images; binary images; rotation invariance; optical character recognition; shape identification; feature extraction; pattern recognition; Image analysis; Experimental result; Grey level image; Image processing; Moment method; Pattern recognition; Density distribution; Character recognition; Spatial resolution; Noisy image
• ISBN: 0852967179; 9780852967171
• ISSN: 0537-9989
• DOI: 10.1049/cp:19990320

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.