IEICE TRANSACTIONS on Information
Detecting Contours in Image Sequences
Summary :
This paper describes a new algorithm for finding the contours of a moving object in an image sequence. A distinctive feature of this algorithm is its complete bottom-up strategy from image data to a consistent contour description. In our algorithm, an input image sequence is immediately converted to a complete set of quasi logical spatio-temporal measures on each pixel, which provide constraints on varying brightness. Then, candidate regions in which to localize the contour are bounded based on consistent grouping among neighboring measures. This reduces drastically the ambiguity of contour location. Finally, Some mid-level constraints on spatial and temporal smoothness of moving boundaries are invoked, and they are combined with these low-level measures in the candidate regions. This is performed efficiently by the regularization over the restricted trajectory of the moving boundary in the candidate regions. Since any quantity is dimensionless, the results are not affected by varying conditions of camera and objects. We examine the efficiency of this algorithm through several experiments on real NTSC motion pictures with dynamic background and natulal textures.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.10 pp.1162-1173
- Publication Date
- 1993/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision Applications)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Kenji NAGAO, Masaki SOHMA, Katsura KAWAKAMI, Shigeru ANDO, "Detecting Contours in Image Sequences" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 10, pp. 1162-1173, October 1993, doi: .
Abstract: This paper describes a new algorithm for finding the contours of a moving object in an image sequence. A distinctive feature of this algorithm is its complete bottom-up strategy from image data to a consistent contour description. In our algorithm, an input image sequence is immediately converted to a complete set of quasi logical spatio-temporal measures on each pixel, which provide constraints on varying brightness. Then, candidate regions in which to localize the contour are bounded based on consistent grouping among neighboring measures. This reduces drastically the ambiguity of contour location. Finally, Some mid-level constraints on spatial and temporal smoothness of moving boundaries are invoked, and they are combined with these low-level measures in the candidate regions. This is performed efficiently by the regularization over the restricted trajectory of the moving boundary in the candidate regions. Since any quantity is dimensionless, the results are not affected by varying conditions of camera and objects. We examine the efficiency of this algorithm through several experiments on real NTSC motion pictures with dynamic background and natulal textures.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_10_1162/_p
Copy
@ARTICLE{e76-d_10_1162,
author={Kenji NAGAO, Masaki SOHMA, Katsura KAWAKAMI, Shigeru ANDO, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Contours in Image Sequences},
year={1993},
volume={E76-D},
number={10},
pages={1162-1173},
abstract={This paper describes a new algorithm for finding the contours of a moving object in an image sequence. A distinctive feature of this algorithm is its complete bottom-up strategy from image data to a consistent contour description. In our algorithm, an input image sequence is immediately converted to a complete set of quasi logical spatio-temporal measures on each pixel, which provide constraints on varying brightness. Then, candidate regions in which to localize the contour are bounded based on consistent grouping among neighboring measures. This reduces drastically the ambiguity of contour location. Finally, Some mid-level constraints on spatial and temporal smoothness of moving boundaries are invoked, and they are combined with these low-level measures in the candidate regions. This is performed efficiently by the regularization over the restricted trajectory of the moving boundary in the candidate regions. Since any quantity is dimensionless, the results are not affected by varying conditions of camera and objects. We examine the efficiency of this algorithm through several experiments on real NTSC motion pictures with dynamic background and natulal textures.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Detecting Contours in Image Sequences
T2 - IEICE TRANSACTIONS on Information
SP - 1162
EP - 1173
AU - Kenji NAGAO
AU - Masaki SOHMA
AU - Katsura KAWAKAMI
AU - Shigeru ANDO
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1993
AB - This paper describes a new algorithm for finding the contours of a moving object in an image sequence. A distinctive feature of this algorithm is its complete bottom-up strategy from image data to a consistent contour description. In our algorithm, an input image sequence is immediately converted to a complete set of quasi logical spatio-temporal measures on each pixel, which provide constraints on varying brightness. Then, candidate regions in which to localize the contour are bounded based on consistent grouping among neighboring measures. This reduces drastically the ambiguity of contour location. Finally, Some mid-level constraints on spatial and temporal smoothness of moving boundaries are invoked, and they are combined with these low-level measures in the candidate regions. This is performed efficiently by the regularization over the restricted trajectory of the moving boundary in the candidate regions. Since any quantity is dimensionless, the results are not affected by varying conditions of camera and objects. We examine the efficiency of this algorithm through several experiments on real NTSC motion pictures with dynamic background and natulal textures.
ER -
English
