Research Article
Fruit Image Recognition Based on Census Transform and Deep Belief Network
@INPROCEEDINGS{10.1007/978-3-030-51103-6_39, author={Qi Xin and Shaohai Hu and Shuaiqi Liu and Hui Lv and Shuai Cong and Qiancheng Wang}, title={Fruit Image Recognition Based on Census Transform and Deep Belief Network}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Fruit image recognition Deep belief network Census transform}, doi={10.1007/978-3-030-51103-6_39} }- Qi Xin
Shaohai Hu
Shuaiqi Liu
Hui Lv
Shuai Cong
Qiancheng Wang
Year: 2020
Fruit Image Recognition Based on Census Transform and Deep Belief Network
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_39
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.
