Abstract
A new segmentation method is presented. The watershed transformation is initially computed starting from all seeds detected as regional minima in the gradient image and a digging cost is associated to each pair of adjacent regions. Digging is performed for each pair of adjacent regions for which the cost is under a threshold, whose value is computed automatically, so originating a reduced set of seeds. Watershed transformation and digging are repeatedly applied, until no more seeds are filtered out. Then, region merging is accomplished, based on the size of adjacent regions.
Chapter PDF
Similar content being viewed by others
References
Lucchese, L., Mitra, S.K.: Color image segmentation: A State-of-the-Art Survey. Proc. of the Indian National Science Academy (INSA-A) 67A(2), 207–221 (2001)
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)
Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Electronic Imaging 13(1), 146–165 (2004)
Wirjadi, O.: Image and video matting: a survey. Fraunhofer Institut für Techno und Wirtschaftsmathematik ITWM 2007, ISSN 1434-9973 Bericht 123 (2007)
Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. In: CVIU, vol. 110, pp. 260–280 (2008)
Shamir, A.: A survey on mesh segmentation techniques. Computer Graphics Forum 27(6), 1539–1556 (2008)
Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation: a survey of Soft Computing Approaches. Int. J. Recent Trends in Engineering 1(2), 250–254 (2009)
Yang, Z., Chung, F.-L., Shitong, W.: Robust fuzzy clustering-based image segmentation. Applied Soft Computing 9, 80–84 (2009)
Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: Proc. Int. Workshop on Image Processing, Real-time Edge and Motion Detection/Estimation, Rennes, France, pp. 12–21 (1979)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. PAMI 13(6), 583–598 (1991)
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)
Frucci, M.: Oversegmentation reduction by flooding regions and digging watershed lines. IJPRAI 20(1), 15–38 (2006)
Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based reasoning for image segmentation by watershed transformation. In: Case-based Reasoning on Images and Signals, vol. 73, pp. 319–352. Springer, Berlin (2007)
Soille, P., Vogt, P.: Morphological segmentation of binary patterns. Pattern Recognition Letters 30(4), 456–459 (2009)
Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Trans. Information Technology in Biomedicine 13(2), 166–173 (2009)
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Frucci, M., di Baja, G.S. (2011). A New Algorithm for Image Segmentation via Watershed Transformation. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24088-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-24088-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24087-4
Online ISBN: 978-3-642-24088-1
eBook Packages: Computer ScienceComputer Science (R0)