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Volume: 28 | Article ID: art00016
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Image Segmentation Using Fuzzy Spatial-Taxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint)
  DOI :  10.2352/ISSN.2470-1173.2016.16.HVEI-121  Published OnlineFebruary 2016
Abstract

Computer vision is typically thought of as an open-universe problem because every possible outcome is unknown. Image segmentation via fuzzy-spatial-taxon-cut reduces image segmentation to a closed-universe problem by assuming a standardized natural-scene-taxonomy, comprised of spatial-taxons. People describe spatial-taxons as thing-like, a group of things or the foreground[2]. They share properties, border ownership in particular, with proto-objects described in biological vision [17]. By defining spatial-taxons in a hierarchy, we operationalize the image segmentation problem into a series of iterative two-class inferences. As described in earlier publications, this method out performs other segmentation methods for well-defined image classes and forms the basis of some commercial image-processing systems. This paper explores how the methodology used to provide the inputs to the low-level color-parsing stage affects overall image segmentation performance by comparing the effects of two methods: fuzzy constraint and Bayes classifier. We discuss how these methods alter the performance the of two-class fuzzy inference system discussed in earlier work.

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Lauren Barghout, "Image Segmentation Using Fuzzy Spatial-Taxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint)in Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-121

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