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A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
August 2008 (vol. 30 no. 8)
pp. 1400-1414
ASCII Text
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Luca Bertelli, Baris Sumengen, B.S. Manjunath, Frédéric Gibou, "A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1400-1414, August, 2008.
 
 
BibTex
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@article{ 10.1109/TPAMI.2007.70785,
author = {Luca Bertelli and Baris Sumengen and B.S. Manjunath and Frédéric Gibou},
title = {A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {8},
issn = {0162-8828},
year = {2008},
pages = {1400-1414},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70785},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
IS - 8
SN - 0162-8828
SP1400
EP1414
EPD - 1400-1414
A1 - Luca Bertelli,
A1 - Baris Sumengen,
A1 - B.S. Manjunath,
A1 - Frédéric Gibou,
PY - 2008
KW - Segmentation
KW - Edge and feature detection
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques.

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Index Terms:
Segmentation, Edge and feature detection
Citation:
Luca Bertelli, Baris Sumengen, B.S. Manjunath, Frédéric Gibou, "A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1400-1414, Aug. 2008, doi:10.1109/TPAMI.2007.70785
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