SIFT Application Separates Motion Characteristics and Identifies Symbols on Tires

Nguyen Ha Huy Cuong, Doan Van Thang, Nguyen Trong Tung, Mai Nhat Tan & Nguyen Thi Thuy Dien 

Conference paper

First Online: 26 April 2023

Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 326)

Abstract

Deep learning techniques have aided in the transfer of content-based image retrieval issues from manually constructed local features like scale-invariant feature transformations (SIFT) to features obtained from convolutional neural networks (CNNs). Existing image-based CNN features, which are taken directly from the entire picture, are not adequate for identifying small areas of overlap. These will have an impact on the performance of partial duplicate picture detection. In this work, we systematized the SIFT characteristics, compared them to other algorithms, and then employed it to separate the motion characteristics and recognize the characters on the tire tread. The experimental findings suggest that after enhancing the input data, the training process of digital characters may be identified with high accuracy.

Source: https://link.springer.com/chapter/10.1007/978-981-19-7513-4_1