Rotation-invariant Histogram of Oriented Gradients for Target Description

CHEN Derong; WANG Wenbin; LIU Bingtai; JIANG Wei; YU Da; GONG Jiulu

doi:10.11999/JEIT150546

Volume 38 Issue 1

Jan. 2016

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CHEN Derong, WANG Wenbin, LIU Bingtai, JIANG Wei, YU Da, GONG Jiulu. Rotation-invariant Histogram of Oriented Gradients for Target Description[J]. Journal of Electronics & Information Technology, 2016, 38(1): 23-28. doi: 10.11999/JEIT150546

Citation:

CHEN Derong, WANG Wenbin, LIU Bingtai, JIANG Wei, YU Da, GONG Jiulu. Rotation-invariant Histogram of Oriented Gradients for Target Description[J]. Journal of Electronics & Information Technology, 2016, 38(1): 23-28. doi: 10.11999/JEIT150546

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Rotation-invariant Histogram of Oriented Gradients for Target Description

doi: 10.11999/JEIT150546

1.
(National Laboratory for Mechatronic and Control, Beijing Institute of Technology, Beijing 100081, China)
2.
(Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China)

Funds:

The Foundations of General Armament Department, Funds of Beijing Institute of Technology

Received Date: 2015-05-11
Rev Recd Date: 2015-09-16
Publish Date: 2016-01-19

Abstract

Abstract

A rotation-invariant feature descripts method called Rotation Invariant Histogram of Oriented Gradients (RI-HOG) is proposed for automatic target recognition. RI-HOG calculates gradient of image first, then the image window is divided into a set of un-overlapped annular regions, called sells, and the Histogram of Gradient (HoG) is used to calculate a feature vector for each cells. After that the HoG of each circle is accumulated to get the main angle of the target area, and then it is rotated due to the main angle to make a normalization of the main angle. At last, the HoG of each circle after rotating is linked to generate the rotation-invariant target feature vector. Experiment results show that target detection method using RI-HOG can find the target under arbitrary rotations. RI-HOG is a rotation-invariant target feature descriptor.
- Automatic target recognition,
- Feature description method,
- Histogram of Gradient (HoG),
- Rotation invariant

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References(17)

References

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