Lapped Block Image Analysis via the Method of Legendre Moments
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Lapped Block Image Analysis via the Method of Legendre Moments Hakim El Fadili D´epartement de physique, Facult´e des Sciences Dhar el Mehraz, Universit´e Sidi Mohamed Ben Abdellah, BP 1796 Atlas, F`es, Morocco Email: el fadili [email protected]
Khalid Zenkouar D´epartement de physique, Facult´e des Sciences Dhar el Mehraz, Universit´e Sidi Mohamed Ben Abdellah, BP 1796 Atlas, F`es, Morocco Email: [email protected]
Hassan Qjidaa D´epartement de physique, Facult´e des Sciences Dhar el Mehraz, Universit´e Sidi Mohamed Ben Abdellah, BP 1796 Atlas, F`es, Morocco Email: [email protected] Received 22 August 2002 and in revised form 16 April 2003 Research investigating the use of Legendre moments for pattern recognition has been performed in recent years. This field of research remains quite open. This paper proposes a new technique based on block-based reconstruction method (BBRM) using Legendre moments compared with the global reconstruction method (GRM). For alleviating the blocking artifact involved in the processing, we propose a new approach using lapped block-based reconstruction method (LBBRM). For the problem of selecting the optimal number of moment used to represent a given image, we propose the maximum entropy principle (MEP) method. The main motivation of the proposed approaches is to allow fast and efficient reconstruction algorithm, with improvement of the reconstructed images quality. A binary handwritten musical character and multi-gray-level Lena image are used to demonstrate the performance of our algorithm. Keywords and phrases: Legendre moments, global image reconstruction method, block-based reconstruction method, maximum entropy principle, blocking artifact, lapped block-based reconstruction method.
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INTRODUCTION
Moments and functions of moments have been extensively employed as the invariant global features of an image in pattern recognition, image classification, target identification, and scene analysis [1, 2, 3, 4, 5]. In the recent years, research investigating the use of moments for pattern reconstruction has been performed. Teh and Chin [6] performed an extensive analysis and comparison of the most common moment definitions, where conventional, Legendre, Zernike, pseudo-Zernike, rotational, and complex moments were all examined in terms of image representation ability, information redundancy, and noise sensitivity. Both analytic and experimental methods were used to characterize the various moment definitions. They concluded that, in terms of overall performances, Zernike and pseudo-Zernike moments outperform the other types.
In general, orthogonal moments are better than other types of moments in terms of information redundancy and image representation. More recently, an important and significant work considering moments for pattern reconstruction was performed by Liao and Pawlak [7]. In this study, the error analysis and characterization of Legendre moments descriptors have been investigated, where several new techniques to increase the accuracy and the efficiency of the moments are pro
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