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AuthorBen Haj Rhouma, Mohamed
AuthorŽunić, Joviša
AuthorYounis, Mohammed Chachan
Available date2020-09-24T08:11:57Z
Publication Date2017
Publication NameComputers and Electronics in Agriculture
ResourceScopus
ISSN1681699
URIhttp://dx.doi.org/10.1016/j.compag.2017.08.029
URIhttp://hdl.handle.net/10576/16288
AbstractIn 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.
Languageen
PublisherElsevier B.V.
SubjectLeaf classification
Moments
Multi-component shapes
Shape
Shape invariants
TitleMoment invariants for multi-component shapes with applications to leaf classification
TypeArticle
Pagination326-337
Volume Number142
dc.accessType Abstract Only


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