Aptoula,E.Yanikoglu,B.2024-05-252024-05-25201339978-147992341-010.1109/ICIP.2013.67383072-s2.0-84897798384https://doi.org/10.1109/ICIP.2013.6738307https://hdl.handle.net/20.500.14517/2301The Institute of Electrical and Electronics Engineers (IEEE) Signal Processing SocietyAlthough plant recognition has become an increasingly popular research topic, it remains nonetheless a scientific and technical challenge. Besides all the difficulties of classic object recognition, such as illumination, viewpoint and scale variations, plants can additionally exhibit visual changes depending on their age and condition, thus demanding a specialized approach. In this paper, we present two descriptors based on mathematical morphology; the first consists of the computation of morphological covariance on the leaf contour profile and the second is an extension of the recently introduced circular covariance histogram, capturing leaf venation characteristics. The effectiveness of both descriptors has been validated with the ImageClef'12 plant identification dataset. © 2013 IEEE.eninfo:eu-repo/semantics/closedAccesscircular covariance histogramfeature extractionmathematical morphologymorphological covariancePlant recognitionConference Object14961499