Automatic correction of the adverse effects of light on fruit surfaces using the shape-from-shading method

Shu Zhang, Jun Lu, Lijuan Shi, Xianfeng Wang and Dejia Hou
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Shu Zhang: College of Science and
Jun Lu: College of Science and
Lijuan Shi: College of Infomatics, Huazhong Agriculture University, Wuhan, P.R. China
Xianfeng Wang: College of Science and
Dejia Hou: College of Science and

Czech Journal of Food Sciences, 2018, vol. 36, issue 1, 37-43

Abstract: In this study, we propose a method for correcting the adverse effects produced by the cur vature of fruit objects in images acquired by cameras in machine vision systems. The areas near the edge are darker in acquired images than in the centre, which results in many difficulties for subsequent analyses. In this paper, the fruit object was considered as a Lambertian surface. The light intensity was analysed and the height and normal on fruit surface was deduced based on the shape-from-shading (SFS) algorithm. The geometric correction factors were calculated and the adverse effects of light intensity were corrected on the fruit surface. The proposed method was evaluated on a test set of four types of fruit. The results show that the non-uniformity of the greyscale value on the fruit surface fell by 35.5% after correction, and the ref lectance in the central area of fruit is similar to that of the peripheral areas when using the proposed method. The experiments prove that our method allowed homogenisation of the greyscale level of the pixels belonging to the same class, regardless of where they are on the fruit surface, which will facilitate subsequent classification tasks.

Keywords: fruit inspection; illumination correction; machine vision (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:36:y:2018:i:1:id:105-2017-cjfs

DOI: 10.17221/105/2017-CJFS

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