Edge detection algorithm for omnidirectional images, based on superposition laws on Blach's sphere and quantum entropy
Ezzaki, Ayoub (Mohammed V University in Rabat. Physics Department)
Benkhedra, Dirar (Mohammed V University in Rabat. Mathematic Department)
El Ansari, Mohamed (My Ismail University in Meknes (Marroc). Computer Science Department)
Masmoudi, Lhoussaine (Mohammed V University in Rabat. Physics Department)

Date:	2021
Abstract:	This paper presents an edge detection algorithm for omnidirectional images based on superposition law onBloch's sphere and quantum local entropy. Omnidirectional vision system has become an essential tool incomputer vision, duo to its large field of view. However, classical image processing algorithms are not suitable to be applied directly in this type of images without taking into account the spatial information around each pixel. To show the performance of the proposed method, a set of experimentation was done on synthetic and real images devoted to agriculture applications. Later, Fram & Deutsh criterion has been adopted to evaluate its performance against three algorithms proposed on the literature and developed for omnidirectional images. The results show a good performance of the proposed method in term of edge quality, edge community and sensibility to noise.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Edge detection ; Omnidirectional images ; Quantum image processing ; Quantum entropy ; Image analysis and processing ; Computer vision
Published in:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 20 Núm. 1 (2021) , p. 70-83 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v20-n1-Ezzai
DOI: 10.5565/rev/elcvia.1338

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