Novel applications of discrete mereotopology to mathematical morphology

Gabriel Landini; Antony Galton; David Randell; Shereen Fouad

doi:10.1016/j.image.2019.04.018

Novel applications of discrete mereotopology to mathematical morphology

Gabriel Landini^*, Antony Galton, David Randell, Shereen Fouad

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing)where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology.

Original language	English
Pages (from-to)	109-117
Number of pages	9
Journal	Signal Processing: Image Communication
Volume	76
Early online date	27 Apr 2019
DOIs	https://doi.org/10.1016/j.image.2019.04.018
Publication status	Published - Aug 2019

Bibliographical note

Funding Information:
The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC) , UK through funding under grant EP/M023869/1 ‘Novel context-based segmentation algorithms for intelligent microscopy’.

Keywords

Discrete mereotopology
Image processing
Mathematical morphology
Spatial reasoning

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10.1016/j.image.2019.04.018

http://www.open-access.bcu.ac.uk/7409/1/signal_processing_paper_submitted.pdf

Cite this

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title = "Novel applications of discrete mereotopology to mathematical morphology",

abstract = "This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing)where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology.",

keywords = "Discrete mereotopology, Image processing, Mathematical morphology, Spatial reasoning",

author = "Gabriel Landini and Antony Galton and David Randell and Shereen Fouad",

note = "Funding Information: The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC) , UK through funding under grant EP/M023869/1 {\textquoteleft}Novel context-based segmentation algorithms for intelligent microscopy{\textquoteright}. ",

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doi = "10.1016/j.image.2019.04.018",

language = "English",

volume = "76",

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T1 - Novel applications of discrete mereotopology to mathematical morphology

AU - Landini, Gabriel

AU - Galton, Antony

AU - Randell, David

AU - Fouad, Shereen

N1 - Funding Information: The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC) , UK through funding under grant EP/M023869/1 ‘Novel context-based segmentation algorithms for intelligent microscopy’.

PY - 2019/8

Y1 - 2019/8

N2 - This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing)where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology.

AB - This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing)where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology.

KW - Discrete mereotopology

KW - Image processing

KW - Mathematical morphology

KW - Spatial reasoning

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UR - https://www.sciencedirect.com/science/article/pii/S0923596518311214?via%3Dihub

U2 - 10.1016/j.image.2019.04.018

DO - 10.1016/j.image.2019.04.018

M3 - Article

AN - SCOPUS:85065235448

SN - 0923-5965

VL - 76

SP - 109

EP - 117

JO - Signal Processing: Image Communication

JF - Signal Processing: Image Communication

ER -

Novel applications of discrete mereotopology to mathematical morphology

Abstract

Bibliographical note

Keywords

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