



@conference {2327,
	title = {Watershed framework to region-based image segmentation},
	booktitle = {Proceedings - International Conference on Pattern RecognitionProc. Int. Conf. Pattern Recognit.},
	series = {2008 19th International Conference on Pattern Recognition, ICPR 2008},
	year = {2008},
	note = {Conference code: 81859Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusArt. No.: 4761587CODEN: PICRELanguage of Original Document: EnglishCorrespondence Address: Monteiro, F. C.; INEB - Instituto de Engenharia Biom{\'e}dicaPortugal; email: monteiro@ipb.ptReferences: Callaghan, R.O., Bull, D., Combined morphologicalspectral unsupervised image segmentation (2005) IEEE Transactions on Image Processing, 14 (1), pp. 49-62;Comaniciu, D., Meer, P., Mean shift: A robust approach toward feature space analysis (2002) IEEE Trans. Pattern Analysis and Machine Intelligence, 24 (5), pp. 603-619; 
Cour, T., Benezit, F., Shi, J., Spectral segmentation with multiscale graph decomposition (2005) Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2, pp. 1124-1131. , June; 
De Bock, J., De Smet, P., Philips, W., Image segmentation using watersheds and normalized cuts (2005) Proceedings of SPIE - The International Society for Optical Engineering, 5675, pp. 164-173. , Imaging - Vision Geometry XIII; 
Deng, Y., Manjunath, B., Unsupervised segmentation of color-texture regions in images and video (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (8), pp. 800-810; 
Martin, D., Fowlkes, C., Tal, D., Malik, J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics (2001) Proc. of IEEE Int. Conference on Computer Vision, 2, pp. 416-423. , http://www.cs.berkeley.edu/projects/vision/grouping/segbench/, online at; 
Monteiro, F., (2007) Region-based Spatial and Temporal Image Segmentation, , PhD thesis, Faculdade de Engenharia da Universidade do Porto, Portugal
},
	month = {2008///},
	address = {Tampa, FL},
	abstract = {This paper proposes a new framework to image segmentation which combines edge- and region-based information with spectral techniques through the morphological algorithm of watersheds. A pre-processing step is used to reduce the spatial resolution without losing important image information. An initial partitioning of the image into primitive regions is set by applying a rainfalling watershed algorithm on the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. The latter process uses a region-based similarity graph representation of the image regions. The experimental results clearly demonstrate the effectiveness of the proposed approach to produce simpler segmentations and to compare favourably with state-of-the-art methods. {\textcopyright} 2008 IEEE.},
	keywords = {Computationally efficient, Edge detection, Graph representation, Image gradients, Image information, Image regions, Image segmentation, Landforms, Pre-processing step, Process use, Rainfalling watershed, Region segmentation, Region-based, Spatial resolution, Spectral techniques, State-of-the-art methods, Watersheds},
	isbn = {10514651 (ISSN); 9781424421756 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957964931\&partnerID=40\&md5=3c1ab50936b31063f75f5b6337ed3335},
	author = {Monteiro, F.C. and Campilho, A.}
}
