Abstract
Fruit image recognition plays an important role in the fields of smart agriculture and digital medical treatment. In order to overcome the disadvantage of the deep belief networks (DBN) that ignores the local structure of the image and is difficult to learn the local features of the image, and considering that the fruit image is affected by the change of illumination, we propose a new fruit image recognition algorithm based on Census transform and DBN. Firstly, the texture features of fruit images are extracted by Census transform. Secondly, DBN is trained by Census features of fruit images. Finally, DBN is used for fruit image recognition. The experimental results show that the proposed algorithm has a strong feature learning ability, and the recognition performance is better than the traditional recognition algorithm.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China under Grant 61572063 and 61401308, Natural Science Foundation of Hebei Province under Grant F2020201025, F2016201142, F2019201151 and F2018210148, Science Research Project of Hebei Province under Grant BJ2020030 and QN2017306, Foundation of President of Hebei University under Grant XZJJ201909. This work was also supported by the High-Performance Computing Center of Hebei University.
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Xin, Q., Hu, S., Liu, S., Lv, H., Cong, S., Wang, Q. (2020). Fruit Image Recognition Based on Census Transform and Deep Belief Network. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_39
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