Abstract
In this paper, we propose a framework for the segmentation of multicomponent images. The specific framework we aim at contains different steps in which all components of the multicomponent image are processed simultaneously, accounting for the correlation between the image components. The framework contains the following steps: a) to initiate, a pixel-based, spectral clustering procedure is applied. b) to include spatial information, a model-based region-merging technique is used, applying a multinormal model for the coefficient regions, and estimating the model parameters using Maximum Likelihood principles; c)the model allows to treat noise that might be present efficiently; d) a multiscale version of the framework is established by repeating the same procedure at different resolution levels of the original image. e) Then, a link between the different levels is established by constructing a hierarchy between the regions at different levels. In this work, we will demonstrate the performance of the framework for segmentation purposes. The procedure is performed on color images and multispectral remote sensing images.
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Driesen, J., Scheunders, P. (2008). A Multicomponent Image Segmentation Framework. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_53
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DOI: https://doi.org/10.1007/978-3-540-88458-3_53
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