Symmetric region growing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as Symmetric Region Growing (SymRG), leads to a single-pass region-growing approach applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Finally, by-products of this general paradigm are algorithms for fast connected-component labeling and cavity deletion. The paper gives theoretical results and 3-D image examples.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Pages96-99
Number of pages4
Volume2
StatePublished - 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

Fingerprint

Image segmentation
Merging
Labeling
Byproducts
Data storage equipment

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Wan, S. Y., & Higgins, W. E. (2000). Symmetric region growing. In IEEE International Conference on Image Processing (Vol. 2, pp. 96-99)
Wan, S. Y. ; Higgins, William Evan. / Symmetric region growing. IEEE International Conference on Image Processing. Vol. 2 2000. pp. 96-99
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Wan, SY & Higgins, WE 2000, Symmetric region growing. in IEEE International Conference on Image Processing. vol. 2, pp. 96-99, International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada, 9/10/00.

Symmetric region growing. / Wan, S. Y.; Higgins, William Evan.

IEEE International Conference on Image Processing. Vol. 2 2000. p. 96-99.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as Symmetric Region Growing (SymRG), leads to a single-pass region-growing approach applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Finally, by-products of this general paradigm are algorithms for fast connected-component labeling and cavity deletion. The paper gives theoretical results and 3-D image examples.

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Wan SY, Higgins WE. Symmetric region growing. In IEEE International Conference on Image Processing. Vol. 2. 2000. p. 96-99