Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/21328

TitleHigh-performance recognition of corneas and crystalline lens from highly noisy images
Author(s)Mariano, Artur Miguel Matos
Franco, Sandra
KeywordsEdge-detection
Corneal detection
Cristalline detection
Noisy images
Crystalline detection
Edge-detetion
Issue date2012
PublisherCESER Publications
JournalInternational Journal of Imaging and Robotics
Abstract(s)This paper proposes an approach to human-eye cornea and crystalline lens edges acceptance in digital images, when captured in environments that returns highly-noisy images, in which edge-detection algorithms do not perform very well. The proposed approach is supported by previously known properties of the cornea and the crystalline, beyond specific filters based on such properties. After being computationally implemented, these properties are imposed on the set of the acquired data, allowing conclusions about what data is correct, wrong and possibly wrong. While the stated properties will always produce true results, the filters can cause data losses, and may thus be implemented depending on the context and on the desired accuracy. By comparing this approach with the conventional one, which relies on suitable digital filters, there were observed large speed-ups, for relatively equal final detections.
TypeArticle
URIhttp://hdl.handle.net/1822/21328
ISSN0974-0627
2231-525X
Publisher versionhttp://www.ceserp.com/
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CDF - OCV - Artigos/Papers (with refereeing)

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