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Reference TypeConference Paper (Conference Proceedings)
Last Update2013: (UTC)
Metadata Last Update2020: (UTC) administrator
Citation KeyRauberFalcSpinReze:2013:InSeIm
TitleInteractive segmentation by image foresting transform on superpixel graphs
Access Date2022, Jan. 24
Number of Files1
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Author1 Rauber, Paulo Eduardo
2 Falc„o, Alexandre Xavier
3 Spina, Thiago Vallin
4 Rezende, Pedro Jussieu de
Affiliation1 University of Campinas (UNICAMP)
2 University of Campinas (UNICAMP)
3 University of Campinas (UNICAMP)
4 University of Campinas (UNICAMP)
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
DateAug. 5-8, 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-04 21:42:45 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsgraph-based image segmentation
image foresting transform
robot users
interactive segmentation
AbstractThere are many scenarios in which user interaction is essential for effective image segmentation. In this paper, we present a new interactive segmentation method based on the Image Foresting Transform (IFT). The method oversegments the input image, creates a graph based on these segments (superpixels), receives markers (labels) drawn by the user on some superpixels and organizes a competition to label every pixel in the image. Our method has several interesting properties: it is effective, efficient, capable of segmenting multiple objects in almost linear time on the number of superpixels, readily extendable through previously published techniques, and benefits from domain-specific feature extraction. We also present a comparison with another technique based on the IFT, which can be seen as its pixel-based counterpart. Another contribution of this paper is the description of automatic (robot) users. Given a ground truth image, these robots simulate interactive segmentation by trained and untrained users, reducing the costs and biases involved in comparing segmentation techniques.
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