Abstract
We present a supervised segmentation scheme in which a Bayesian approach incorporating a pyramid data structure is used. This formulation leads to a significant simplification of the Spann and Wilson quadtree segmentation algorithm [7] under the assumption that image classes are normally distributed. A method for efficiently acquiring the parameters of class distributions at each resolution level has been developed. It involves estimating the class statistics on training sites at full image resolution. The corresponding parameters at lower resolutions are computed by predetermined scaling factors. The segmentation scheme is validated on synthetic data and natural textures obtained from the Brodatz album [1].
Supported by a scholarship from the Croucher Foundation
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© 1991 Springer-Verlag London Limited
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Ng, I., Kittler, J., Illingworth, J. (1991). Supervised Segmentation Using a Multiresolution Data Representation. In: Mowforth, P. (eds) BMVC91. Springer, London. https://doi.org/10.1007/978-1-4471-1921-0_6
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DOI: https://doi.org/10.1007/978-1-4471-1921-0_6
Publisher Name: Springer, London
Print ISBN: 978-3-540-19715-7
Online ISBN: 978-1-4471-1921-0
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