Joint graph cut and relative fuzzy connectedness image segmentation algorithm
نویسندگان
چکیده
منابع مشابه
Joint graph cut and relative fuzzy connectedness image segmentation algorithm
We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's s...
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In this chapter, we will review the current state of knowledge on regionbased digital image segmentation methods. More precisely, we will concentrate on the four families of such algorithms: (a) The leading theme here will be the framework of fuzzy connectedness (FC) methods. (b) We will also discuss in detail the family of graph cut (GC) methods and their relations to the FC family of algorith...
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Image segmentation—the process of defining objects in images—remains the most challenging problem in image processing despite decades of research. Many general methodologies have been proposed to date to tackle this problem. An emerging framework that has shown considerable promise recently is that of fuzzy connectedness. Images are by nature fuzzy. Object regions manifest themselves in images ...
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ژورنال
عنوان ژورنال: Medical Image Analysis
سال: 2013
ISSN: 1361-8415
DOI: 10.1016/j.media.2013.06.006