Research on signal processing technology optimization of contact image sensor based on BP neural network algorithm

Abstract

In order to improve the signal processing technology of contact image sensor, the optimization of image processing technology is carried out from the aspects of feature extraction, fine-grained semantic feature generation and semantic analysis matching optimization. To solve the problem of inaccurate feature extraction, a multi-scale feature representation algorithm for food images is proposed. By extracting the features of multi-scale convolution layer, and according to the food image, the feature extraction process is dispersed into each convolution process. By comparing the features of the layers with the image library, the most accurate features are selected for transmission. To solve the problem of conservative vocabulary and poor generalization performance of generated sentences, a fine-grained image semantic analysis algorithm based on subword segmentation is proposed. The results show that compared with the mainstream methods, the proposed method has improved in varying degrees on the four evaluation indicators. The research provides a reference for the optimization of image sensor signal processing technology and the wide application of BP neural network algorithm.