Open AccessAutomatic detection of individual and touching moths from trap images by combining contour‐based and region‐based segmentation
Author(s) -
Bakkay Mohamed Chafik,
Chambon Sylvie,
Rashwan Hatem A.,
Lubat Christian,
Barsotti Sébastien
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0086
Subject(s) - artificial intelligence , segmentation , computer science , pattern recognition (psychology) , cluster analysis , image segmentation , computer vision , convex hull , trap (plumbing) , active contour model , biometrics , image (mathematics) , regular polygon , mathematics , geography , geometry , meteorology
Insect detection is one of the most challenging problems of biometric image processing. This study focuses on developing a method to detect both individual insects and touching insects from trap images in extreme conditions. This method is able to combine recent approaches on contour‐based and region‐based segmentation. More precisely, the two contributions are: an adaptive k ‐means clustering approach by using the contour's convex hull and a new region merging algorithm. Quantitative evaluations show that the proposed method can detect insects with higher accuracy than that of the most used approaches.