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. Attached Files Name Description MIMEType Size Downloads Title A fast orientation estimation approach of natural images Author(s) Cao, Zhiqiang Liu, Xilong Gu, Nong 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 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 Collections: Centre for Intelligent Systems Research 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 01 Feb 2017, 15:49:31 EST Thu, 02 Feb 2017, 04:01:40 EST Tue, 04 Apr 2017, 06:04:25 EST Mon, 19 Jun 2017, 13:57:43 EST Mon, 19 Jun 2017, 14:00:55 EST Mon, 19 Jun 2017, 14:01:41 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 102 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Wed, 01 Feb 2017, 15:49:31 EST

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