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THE MELLIN CENTRAL PROJECTION TRANSFORM

Part of: Artificial intelligence (68Txx)

Published online by Cambridge University Press:  07 March 2017

JIANWEI YANG Open the ORCID record for JIANWEI YANG [Opens in a new window] ,
LIANG ZHANG  and
ZHENGDA LU
JIANWEI YANG*
Affiliation:
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China, 210044 email [email protected]
LIANG ZHANG
Affiliation:
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China, 210044 email [email protected]
ZHENGDA LU
Affiliation:
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China, 210044 email [email protected]
*
✉[email protected]
Rights & Permissions [Opens in a new window]

Abstract

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The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features.

Keywords

Mellin central projection transformMellin transformaffine invariantsfeature extraction

MSC classification

Primary: 68T10: Pattern recognition, speech recognition
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 256 - 264
Copyright
© 2017 Australian Mathematical Society 

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