CAIP 2005: Computer Analysis of Images and Patterns pp 538-545 | Cite as
Morphological Refinement of an Image Segmentation
Conference paper
Abstract
This paper describes a method to improve a given segmentation result in order to produce a new, refined and more accurate segmented image. The method consists of three phases: shrinking of the input partitions, filtering of the input imagery leading to a mask image, and expansion of the shrunk partitions within the filtered image. The concept is illustrated for the enhancement of a land cover data set using multispectral satellite imagery.
Keywords
Land Cover Image Segmentation Segmentation Result Expansion Phase Mathematical Morphology
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Serra, J.: Image analysis and mathematical morphology. Academic Press, London (1982)MATHGoogle Scholar
- 2.Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg (2003)MATHGoogle Scholar
- 3.Vincent, L.: Efficient computation of various types of skeletons. In: Loew, M. (ed.) Medical Imaging V: Image Processing, vol. 1445, pp. 297–311. SPIE, San Jose (1991)Google Scholar
- 4.Ranwez, V., Soille, P.: Order independent homotopic thinning for binary and grey tone anchored skeletons. Pattern Recognition Letters 23, 687–702 (2002)MATHCrossRefGoogle Scholar
- 5.Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Transactions on Image Processing 2, 176–201 (1993)CrossRefGoogle Scholar
- 6.Soille, P.: Beyond self-duality in morphological image analysis. Image and Vision Computing 23, 249–257 (2005)CrossRefGoogle Scholar
- 7.Vincent, L.: Morphological area openings and closings for greyscale images. In: Proc. Shape in Picture 1992, NATO Workshop, Driebergen, The Netherlands. Springer, Heidelberg (1992)Google Scholar
- 8.Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing 4, 1153–1160 (1995)CrossRefGoogle Scholar
- 9.Brunner, D., Soille, P.: Iterative area seeded region growing for multichannel image simplification. In: Ronse, C., Najman, L., Decencire, E. (eds.) Mathematical Morphology: 40 Years of Computational Imaging and Vision, vol. 30, pp. 397–406. Kluwer Academic Publishers, Dordrecht (2005)CrossRefGoogle Scholar
- 10.Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Dougherty, E. (ed.) Mathematical morphology in image processing of Optical Engineering, vol. 34, pp. 433–481. Marcel Dekker, New York (1993)Google Scholar
- 11.Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)CrossRefGoogle Scholar
- 12.Büetner, G., Feranec, J., Jaffrain, G.: Corine land cover update 2000, technical guidelines. Technical Report 89, European Environmental Agency, Copenhagen (2000)Google Scholar
- 13.de Lima, V.N., Peedell, S.: Image2000 - the European spatial reference. In: Proc. of 10th EC-GI & GIS Workshop, Warsaw. European Commission, Joint Research Centre, June 23–25 (2004)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2005