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Improved Log-Gabor Wavelet and Cross-covariance PCA for Discriminating Defect and Stem/Calyx of Pear Images

Published: 29 October 2022 Publication History

Abstract

Discriminating pear defects is a challenging issue due to great variations of different kinds of pear skins, a lot of freckles on the pear skins and similar visual characteristics between the pear defect and the stem/calyx. This paper presents a pear surface defect classification method based on improved Log-Gabor filter and cross-covariance PCA (Principal Component Analysis). Firstly, the nonlinear components are added to the coordinate formula of the Log-Gabor filter in order that the filter has a good curvature characteristic. Therefore, the improved Log-Gabor filter can be used to extract the local bending information of pear defect regions or stem/calyx regions effectively. Then, the cross-covariance matrix is defined and decomposed into the singular matrices that retain both auto-covariance information and cross-covariance information. Last, feature matrices of pear defect images are generated by cross-covariance PCA. The feature matrices are used to distinguish the defect pear images from intact pear images. The experimental results of the proposed algorithm on the datasets of several kinds of pears show that the algorithm can effectively distinguish pear defects from stems/calyxes and freckles on the skins of intact pears.

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  1. Improved Log-Gabor Wavelet and Cross-covariance PCA for Discriminating Defect and Stem/Calyx of Pear Images

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    SPML '22: Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning
    August 2022
    309 pages
    ISBN:9781450396912
    DOI:10.1145/3556384
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 29 October 2022

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