Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/6744
Title: Image processing method for automatic discrimination of hoverfly species
Authors: Crnojević, Vladimir
Panić, Marko
Brkljač, Branko 
Ćulibrk, Dubravko 
Ačanski, Jelena 
Vujičić, Ana
Issue Date: 30-Dec-2014
Journal: Mathematical Problems in Engineering
Abstract: © 2014 Vladimir Crnojević et al. An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy.
URI: https://open.uns.ac.rs/handle/123456789/6744
ISSN: 1024123X
DOI: 10.1155/2014/986271
Appears in Collections:Naučne i umetničke publikacije

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