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A fast orientation estimation approach of natural images

Cao, Zhiqiang, Liu, Xilong, Gu, Nong, Nahavandi, Saeid, Xu, De, Zhou, Chao and Tan, Min 2016, A fast orientation estimation approach of natural images, IEEE transactions on systems, man and cybernetics: systems, vol. 46, no. 11, pp. 1589-1597, doi: 10.1109/TSMC.2015.2497253.

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Title A fast orientation estimation approach of natural images
Author(s) Cao, Zhiqiang
Liu, Xilong
Gu, Nong
Nahavandi, Saeid
Xu, De
Zhou, Chao
Tan, Min
Journal name IEEE transactions on systems, man and cybernetics: systems
Volume number 46
Issue number 11
Start page 1589
End page 1597
Total pages 9
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2016-11
ISSN 2168-2216
Keyword(s) Biological simple cell
Differential field
Natural image
Orientation estimation
Science & Technology
Technology
Automation & Control Systems
Computer Science, Cybernetics
Computer Science
Receptive-fields
Striate cortex
Low-level
Filters
Cell
Summary This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises.
Language eng
DOI 10.1109/TSMC.2015.2497253
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090998

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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