Robust edge detector based on anisotropic diffusion-driven process

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dc.contributor.authorMaiseli, Baraka
dc.contributor.authorGao, Huijun
dc.date.accessioned2016-07-21T19:03:39Z
dc.date.available2016-07-21T19:03:39Z
dc.date.issued2015-12
dc.description.abstractEdge detection involves a process to discriminate, highlight, and extract useful image features (edges and contours). In many situations, we prefer an edge detector that distinguishes these features more accurately, and which comfortably deals with a variety of data. Our observations, however, discovered that most edge-defining functionals underperform and generate false edges under poor imaging conditions. Therefore, the current research proposes a robust diffusion-driven edge detector for seriously degraded images. The method is iterative, and suppresses noise while simultaneously marking real edges and deemphasizing false edges. The anisotropic nature of the new functional helps to remove noise and to preserve semantic structures. Even more importantly, the functional exhibits a forward–backward behavior that may sharpen and strengthen edges. Comparisons with some other classical approaches demonstrate superiority of the proposed approach.en_US
dc.identifier.citationMaiseli, B.J. and Gao, H., 2016. Robust edge detector based on anisotropic diffusion-driven process. Information Processing Letters, 116(5), pp.373-378.en_US
dc.identifier.doi10.1016/j.ipl.2015.12.003
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3387
dc.language.isoenen_US
dc.subjectEdge detectoren_US
dc.subjectPerona–Maliken_US
dc.subjectImage restorationen_US
dc.subjectInformation retrievaen_US
dc.titleRobust edge detector based on anisotropic diffusion-driven processen_US
dc.typeJournal Article, Peer Revieweden_US
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