A unifying approach to moment-based shape orientation and symmetry classification

Tzimiropoulos, G., Mitianoudis, N. and Stathaki, T. (2009) A unifying approach to moment-based shape orientation and symmetry classification. IEEE Transactions on Image Processing, 18 (1). pp. 125-139. ISSN 1057-7149

Full content URL: http://dx.doi.org/10.1109/TIP.2008.2007050

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Item Type:Article
Item Status:Live Archive


In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods. © 2008 IEEE.

Keywords:Fourier analysis, Fourier series, Fourier transforms, Frequency domain analysis, Harmonic analysis, Method of moments, Signal reconstruction, Complex moments, Geometric moments, Polar Fourier transform, Shape orientation, Singular value decomposition (SVD), Symmetry classification, Singular value decomposition, algorithm, article, artificial intelligence, automated pattern recognition, computer assisted diagnosis, image enhancement, methodology, motion, reproducibility, sensitivity and specificity, Algorithms, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8738
Deposited On:20 May 2013 17:13

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