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Investigating the impact of the COVID-19 pandemic on the nutritional status of infants and toddlers: insights from China 期刊论文  OAI收割
POPULATION HEALTH METRICS, 2024, 卷号: 22, 期号: 1, 页码: 31
作者:  Gu, Lijuan;  Yang, Linsheng;  Li, Hairong
  |  收藏  |  浏览/下载：5/0  |  提交时间：2024/12/20
A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文  OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Hu S.; Zhu M.; Wu C.; Song H.-J.
收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25
In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.