期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2021
卷号：20
期号：1
页码：70-83
DOI：10.5565/rev/elcvia.1338
出版社：Centre de Visió per Computador
摘要：This paper presents an edge detection algorithm for omnidirectional images based on superposition law on Bloch’s sphere and quantum local entropy. Omnidirectional vision system has become an essential tool in computer 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.
其他摘要：This paper presents an edge detection algorithm for omnidirectional images based on superposition law on Bloch’s sphere and quantum local entropy. Omnidirectional vision system has become an essential tool in computer 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.
