Authors: Ben Haj Rhouma, Mohamed
Žunić, Joviša 
Younis, Mohammed Chachan
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Moment invariants for multi-component shapes with applications to leaf classification
Journal: Computers and Electronics in Agriculture
Volume: 142
First page: 326
Last page: 337
Issue Date: 1-Nov-2017
Rank: M21
ISSN: 0168-1699
DOI: 10.1016/j.compag.2017.08.029
Abstract: 
In this paper we introduce seven new invariants for multi-component shapes, and apply them to the leaf classification problem. One of the new invariants is an area based analogue of the already known boundary based anisotropy measure, defined for the multi-component shapes (Rosin and Žunić, 2011). The other six invariants are completely new. They are derived following the concept of the geometric interpretation (Xu and Li, 2008) of the first two Hu moment invariants (Hu, 1961). All the invariants introduced are computable from geometric moments corresponding to the shape components. This enables an easy and straightforward computation of translation, rotation, and scaling invariants. Also, being area based, the new invariants are robust to noise and mild deformations. Several desirable properties of the new invariants are discussed and evaluated experimentally on a number of synthetic examples. The usefulness of the new multi-component shape invariants, in the shape based object analysis tasks, is demonstrated on a well-known leaf data set.
Keywords: Leaf classification | Moments | Multi-component shapes | Shape | Shape invariants
Publisher: Elsevier

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