Abstract
The \(\alpha \)–tree represents an image as hierarchical set of \(\alpha \)-connected components. Computation of \(\alpha \)–trees suffers from high computational and memory requirements compared with similar component tree algorithms such as max–tree. Here we introduce a novel \(\alpha \)–tree algorithm using (1) a flooding algorithm for computational efficiency and (2) tree size estimation (TSE) for memory efficiency. In TSE, an exponential decay model was fitted to normalized tree sizes as a function of the normalized root mean squared deviation (NRMSD) of edge-dissimilarity distributions, and the model was used to estimate the optimum memory allocation size for \(\alpha \)–tree construction. An experiment on 1256 images shows that our algorithm runs 2.27 times faster than Ouzounis and Soille’s thanks to the flooding algorithm, and TSE reduced the average memory allocation of the proposed algorithm by 40.4%, eliminating unused allocated memory by 86.0% with a negligible computational cost.
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This paper is based on research developed in the DSSC Doctoral Training Programme, co-funded through a Marie Skłodowska-Curie COFUND (DSSC 754315).
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You, J., Trager, S.C., Wilkinson, M.H.F. (2019). A Fast, Memory-Efficient Alpha-Tree Algorithm Using Flooding and Tree Size Estimation. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_20
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