Acta Optica Sinica, Volume. 37, Issue 1, 133002(2017)

Fabric Image Registration Based on Affine Transform and Levenberg-Marquardt Algorithm

Zhang Biao1、*, Zou Zhe1, Chen Shujie1, Shen Huiliang1, Shao Sijie2, and Xin Haozhong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    In order to solve the problem of color difference analysis for textile fabric images captured by a multispectral system, a new method of image registration based on affine transform and Levenberg-Marquardt (LM) algorithm is proposed. In a registration perspective, the standard image and the batch image are first matched by the proposed method and then taken for analysis of color difference. The deformation of the textile image captured by the multispectral system, including translation, rotation, scaling and shearing, conforms to the classic model of affine transform. To estimate the transform matrix, the proposed method first calculates the integral curves of the log-polar magnitude spectra of the two images for mapping. The original problem of solving the transform matrix is then converted to a numerical problem of nonlinear least squares fitting where the LM algorithm is employed for optimal value searching. Besides, the block registration is introduced to achieve more accurate registration. Experimental results show better registration accuracy of the proposed algorithm compared with the traditional Fourier-Mellin algorithm as well as the scale invariant feature transform based feature point matching method. It also solves the registration problem for textile images with periodic patterns effectively, which contributes to the following process of color difference evaluation.

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    Zhang Biao, Zou Zhe, Chen Shujie, Shen Huiliang, Shao Sijie, Xin Haozhong. Fabric Image Registration Based on Affine Transform and Levenberg-Marquardt Algorithm[J]. Acta Optica Sinica, 2017, 37(1): 133002

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    Paper Information

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    Received: Jul. 19, 2016

    Accepted: --

    Published Online: Jan. 13, 2017

    The Author Email: Biao Zhang (bz@zju.edu.cn)

    DOI:10.3788/aos201737.0133002

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