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Shape prior based Image segmentation using a log distance — theta model

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dc.contributor.author Rodrigo, JS
dc.contributor.author Rodrigo,R
dc.date.accessioned 2013-10-21T02:13:05Z
dc.date.available 2013-10-21T02:13:05Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/8329
dc.description.abstract Image segmentation is a popular topic in computer vision. Incorporation of prior shape knowledge into this process and the subsequent extraction of objects demonstrating shape characteristics similar to the priors are being extensively researched. Major challenges include the judgment of parameters such as scale and rotation as well tolerating object distortions such as occlusions and noise. This paper proposes a novel framework to successfully overcome these by using a log distance-theta domain mapping which greatly simplifies rotation, scaling and provides an opportunity to incorporate a decision threshold.
dc.language en
dc.title Shape prior based Image segmentation using a log distance — theta model
dc.type Conference-Abstract
dc.identifier.year 2009
dc.identifier.conference Research for Industry
dc.identifier.place Faculty of Engineering, University of Moratuwa
dc.identifier.pgnos 200-202
dc.identifier.proceeding 15th Annual symposium on Research and Industry


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