NDLI: Fast interactive segmentation of natural images using the image foresting transform

Content Provider	IEEE Xplore Digital Library
Author	Spina, T.V. Montoya-Zegarra, J.A. Falcao, A.X. Miranda, P.A.V.
Copyright Year	2009
Description	Author affiliation: Institute of Computing - University of Campinas (UNICAMP), C.P. 6176, 13084-971, SP, Brazil (Spina, T.V.; Montoya-Zegarra, J.A.; Falcao, A.X.; Miranda, P.A.V.)
Abstract	This paper presents an unified framework for fast interactive segmentation of natural images using the image foresting transform (IFT) — a tool for the design of image processing operators based on connectivity functions (path-value functions) in graphs derived from the image. It mainly consists of three tasks: recognition, enhancement, and extraction. Recognition is the only interactive task, where representative image properties for enhancement and the object's location for extraction are indicated by drawing a few markers in the image. Enhancement increases the dissimilarities between object and background for more effective object extraction, which completes segmentation. We show through extensive experiments that, by exploiting the synergism between user and computer for recognition and enhancement, respectively, as a separated step from recognition and extraction, respectively, one can reduce user involvement with better accuracy. We also describe new methods for enhancement based on fuzzy classification by IFT and for feature selection and/or combination by genetic programming.
Starting Page	1
Ending Page	8
File Size	1411957
Page Count	8
File Format	PDF
ISBN	9781424432974
DOI	10.1109/ICDSP.2009.5201044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Genetic programming Pixel Process design Image processing Image recognition Humans Tree graphs Filtering Shape multiscale image filtering Graph-based image segmentation image feature selection and/or combination genetic programming fuzzy classification differential image foresting transform
Content Type	Text
Resource Type	Article

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