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dc.contributor.authorBlackledge, J. M.en_US
dc.contributor.authorDubovitskiy, D. A.en_US
dc.contributor.editorWen Tang and John Collomosseen_US
dc.date.accessioned2014-01-31T20:06:40Z
dc.date.available2014-01-31T20:06:40Z
dc.date.issued2009en_US
dc.identifier.isbn978-3-905673-71-5en_US
dc.identifier.urihttp://dx.doi.org/10.2312/LocalChapterEvents/TPCG/TPCG09/041-048en_US
dc.description.abstractWe present an approach to object detection and recognition in a digital image using a classification method that is based on the application of a set of features that include fractal parameters such as the Lacunarity and Fractal Dimension. The principal issues associated with object recognition are presented and a self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory considered. The methods discussed, and the 'system' developed, have a range of applications in 'machine vision' and in this publication, we focus on the development and implementation of a skin cancer screening system that can be used in a general practice by non-experts to 'filter' normal from abnormal cases so that in the latter case, a patient can be referred to a specialist. The paper provides an overview of the system design and includes a link from which interested readers can download and use a demonstration version of the system developed to date.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): F.2.2; I.5.4 [Analysis of Algorithms and problem complexity, Pattern Recognition]: Pattern matching, Computer visionen_US
dc.titleTexture Classification using Fractal Geometry for the Diagnosis of Skin Cancersen_US
dc.description.seriesinformationTheory and Practice of Computer Graphicsen_US


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