IEICE TRANSACTIONS on Information
A Coarse to Fine Image Segmentation Method
Summary :
The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E80-D No.7 pp.726-732
- Publication Date
- 1997/07/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Shanjun ZHANG, "A Coarse to Fine Image Segmentation Method" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 7, pp. 726-732, July 1997, doi: .
Abstract: The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_7_726/_p
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@ARTICLE{e80-d_7_726,
author={Shanjun ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Coarse to Fine Image Segmentation Method},
year={1997},
volume={E80-D},
number={7},
pages={726-732},
abstract={The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - A Coarse to Fine Image Segmentation Method
T2 - IEICE TRANSACTIONS on Information
SP - 726
EP - 732
AU - Shanjun ZHANG
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 1997
AB - The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.
ER -
English
