Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
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DOI:
10.1016/j.media.2013.06.006
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发表时间:
2013-12
影响因子:
10.9
通讯作者:
Udupa, Jayaram K.
中科院分区:
文献类型:
--
作者:
Ciesielski, Krzysztof Chris;Miranda, Paulo A. V.;Falcao, Alexandre X.;Udupa, Jayaram K.
We introduce an image segmentation algorithm, called , 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 preserves robustness of RFC with respect to the seed choice (thus, avoiding “shrinking problem” of GC), while keeping GC’s stronger control over the problem of “leaking though poorly defined boundary segments.” The analysis of is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in running in a time close to linear. Experimental comparison of to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of over these other methods, resulting in a rank ordering of .
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影响因子:
4.5
作者:
Ciesielski, Krzysztof Chris;Udupa, Jayaram K.
通讯作者:
Udupa, Jayaram K.
DOI:
10.1109/tpami.2004.60
发表时间:
2004-09-01
影响因子:
23.6
作者:
Boykov, Y;Kolmogorov, V
通讯作者:
Kolmogorov, V
DOI:
10.1006/gmip.1998.0475
发表时间:
1998-07-01
期刊:
GRAPHICAL MODELS AND IMAGE PROCESSING
影响因子:
--
作者:
Falcao, AX;Udupa, JK;Lotufo, RDA
通讯作者:
Lotufo, RDA
影响因子:
2
作者:
Ciesielski, Krzysztof Chris;Udupa, Jayaram K.;Miranda, P. A. V.
通讯作者:
Miranda, P. A. V.
DOI:
10.1109/tpami.2004.1262177
发表时间:
2004-02-01
影响因子:
23.6
作者:
Kolmogorov, V;Zabih, R
通讯作者:
Zabih, R