Publication

A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation

You, J., Trager, S. & Wilkinson, M. H. F., 31-May-2019, Mathematical Morphology and Its Applications to Signal and Image Processing. Burgeth, B., Kleefeld, A., Naegel, B., Passat, N. & Perret, B. (eds.). Cham: Springer, p. 256-267 (Lecture Notes in Computer Science; vol. 11564).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

APA

You, J., Trager, S., & Wilkinson, M. H. F. (2019). A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. In B. Burgeth , A. Kleefeld , B. Naegel , N. Passat , & B. Perret (Eds.), Mathematical Morphology and Its Applications to Signal and Image Processing (pp. 256-267). (Lecture Notes in Computer Science; Vol. 11564). Cham: Springer. https://doi.org/10.1007/978-3-030-20867-7_20

Author

You, Jiwoo ; Trager, Scott ; Wilkinson, M.H.F. / A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. Mathematical Morphology and Its Applications to Signal and Image Processing. editor / B. Burgeth ; A. Kleefeld ; B. Naegel ; N. Passat ; B. Perret . Cham : Springer, 2019. pp. 256-267 (Lecture Notes in Computer Science).

Harvard

You, J, Trager, S & Wilkinson, MHF 2019, A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. in B Burgeth , A Kleefeld , B Naegel , N Passat & B Perret (eds), Mathematical Morphology and Its Applications to Signal and Image Processing. Lecture Notes in Computer Science, vol. 11564, Springer, Cham, pp. 256-267, International Symposium on Mathematical Morphology, Saarbrücken, Germany, 08/07/2019. https://doi.org/10.1007/978-3-030-20867-7_20

Standard

A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. / You, Jiwoo; Trager, Scott; Wilkinson, M.H.F.

Mathematical Morphology and Its Applications to Signal and Image Processing. ed. / B. Burgeth ; A. Kleefeld ; B. Naegel ; N. Passat ; B. Perret . Cham : Springer, 2019. p. 256-267 (Lecture Notes in Computer Science; Vol. 11564).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Vancouver

You J, Trager S, Wilkinson MHF. A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. In Burgeth B, Kleefeld A, Naegel B, Passat N, Perret B, editors, Mathematical Morphology and Its Applications to Signal and Image Processing. Cham: Springer. 2019. p. 256-267. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-20867-7_20


BibTeX

@inproceedings{0bc52b56bc144b2585a85411e2ad8627,
title = "A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation",
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.",
author = "Jiwoo You and Scott Trager and M.H.F. Wilkinson",
year = "2019",
month = "5",
day = "31",
doi = "10.1007/978-3-030-20867-7_20",
language = "English",
isbn = "978-3-030-20866-0",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "256--267",
editor = "{Burgeth }, B. and {Kleefeld }, { A.} and {Naegel }, { B.} and { Passat }, N. and {Perret }, { B.}",
booktitle = "Mathematical Morphology and Its Applications to Signal and Image Processing",

}

RIS

TY - GEN

T1 - A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation

AU - You, Jiwoo

AU - Trager, Scott

AU - Wilkinson, M.H.F.

PY - 2019/5/31

Y1 - 2019/5/31

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-20867-7_20

DO - 10.1007/978-3-030-20867-7_20

M3 - Conference contribution

SN - 978-3-030-20866-0

T3 - Lecture Notes in Computer Science

SP - 256

EP - 267

BT - Mathematical Morphology and Its Applications to Signal and Image Processing

A2 - Burgeth , B.

A2 - Kleefeld , A.

A2 - Naegel , B.

A2 - Passat , N.

A2 - Perret , B.

PB - Springer

CY - Cham

ER -

ID: 93414662