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, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
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
Automation & Control Systems
Computer Science, Cybernetics
Computer Science
Striate cortex
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

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