IEICE TRANSACTIONS on Information
A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling
Summary :
We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.2 pp.636-645
- Publication Date
- 2012/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.636
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Qingyi GU, Takeshi TAKAKI, Idaku ISHII, "A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 2, pp. 636-645, February 2012, doi: 10.1587/transinf.E95.D.636.
Abstract: We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.636/_p
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@ARTICLE{e95-d_2_636,
author={Qingyi GU, Takeshi TAKAKI, Idaku ISHII, },
journal={IEICE TRANSACTIONS on Information},
title={A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling},
year={2012},
volume={E95-D},
number={2},
pages={636-645},
abstract={We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.},
keywords={},
doi={10.1587/transinf.E95.D.636},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling
T2 - IEICE TRANSACTIONS on Information
SP - 636
EP - 645
AU - Qingyi GU
AU - Takeshi TAKAKI
AU - Idaku ISHII
PY - 2012
DO - 10.1587/transinf.E95.D.636
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2012
AB - We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.
ER -
English
