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A novel rotational invariants target recognition method for rotating motion blurred images
Author(s): Jinhui Lan; Meiling Gong; Mingwei Dong; Yiliang Zeng; Yuzhen Zhang
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Paper Abstract

The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060511 (15 November 2017); doi: 10.1117/12.2286632
Show Author Affiliations
Jinhui Lan, Univ. of Science and Technology Beijing (China)
Meiling Gong, Univ. of Science and Technology Beijing (China)
Mingwei Dong, The High School Affiliated to Univ. of Science and Technology Beijing (China)
Yiliang Zeng, Univ. of Science and Technology Beijing (China)
Yuzhen Zhang, Univ. of Science and Technology Beijing (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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