Image analysis using separable discrete moments of Charlier-Hahn

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• 作者：Mhamed Sayyouri ; Abdeslam Hmimid ; Hassan Qjidaa
• 关键词：Bivariate polynomials ; Charlier ; Hahn invariant moments ; Image reconstruction ; Pattern recognition ; Classification
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：75
• 期：1
• 页码：547-571
• 全文大小：1,524 KB
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• 作者单位：Mhamed Sayyouri (1)
  Abdeslam Hmimid (1)
  Hassan Qjidaa (1)
  
  1. CED-ST; LESSI; Faculty of sciences Dhar El Mehraz, University Sidi Mohamed Ben Abdellah, BP 1796, Fez-Atlas, 30003, Fez, Morocco
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

In this paper, we present a new set of bivariate discrete orthogonal polynomials defined from the product of Charlier and Hahn discrete orthogonal polynomials with one variable. This bivriate polynomial is used to define other set of separable two-dimensional discrete orthogonal moments called Charlier-Hahn’s moments. We also propose the use of the image slice representation methodology for fast computation of Charlier-Hahn’s moments. In this approach the image is decomposed into series of non-overlapped binary slices and each slice is described by a number of homogenous rectangular blocks. Thus, the moments of Charlier-Hahn can be computed fast and easily from the blocks of each slice. A novel set of Charlier-Hahn invariant moments is also presented. These invariant moments are derived algebraically from the geometric invariant moments and their computation is accelerated using an image representation scheme. The presented approaches are tested in several well known computer vision datasets including computational time, image reconstruction, the moment’s invariability and the classification of objects. The performance of these invariant moments used as pattern features for a pattern classification is compared with Charlier, Hahn, Tchebichef-Krawtchouk, Tchebichef-Hahn and Krawtchouk-Hahn invariant moments.