Object Recognition Based on Improved Zernike Moments and SURF
Authors: 
Lei Zhang, Hengliang Shi, Jiexin PuAuthors Info & Claims
Lei Zhang, Hengliang Shi, Jiexin PuAuthors Info & ClaimsPages 116 - 120
Abstract
Since single global or local features can only describe objects partly or unilaterally that may lead to a low recognition rate, object recognition algorithm based on improved Zernike moments and Speeded-up Robust Features (SURF) is proposed. Firstly, the seven improved Zernike moments and SURF descriptor of objects are extracted, and then the two features are fused together with the weights in term of their contribution to the recognition. Secondly, Euclidean distance is calculated to determine the recognition result. Finally, the performance of algorithm is tested by some image data. Experimental results show that the proposed method is robust to scaling transformation, rotation change and noise variation. Compared with the other three ones, the results show that the proposed method has better recognition performance.
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- Object Recognition Based on Improved Zernike Moments and SURF
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Published In
December 2019
263 pages
ISBN:9781450377027
DOI:10.1145/3375998
Copyright © 2019 ACM.
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
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Published: 28 January 2020
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ICNCC 2019
ICNCC 2019: 2019 The 8th International Conference on Networks, Communication and Computing
December 13 - 15, 2019
Luoyang, China
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