Dense center prediction for centerline extraction based on BP network

Chuanqi Gui

doi:10.1117/12.3000906

7 March 2024 Dense center prediction for centerline extraction based on BP network

Chuanqi Gui

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 130880N (2024) https://doi.org/10.1117/12.3000906
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

In the line structured light three-dimensional measurement system, high-precision laser stripe centerline extraction is the key to improving measurement accuracy. The laser line extraction technology based on neural network can automatically detect and extract laser lines with high accuracy and robustness. In this paper, a BP neural network containing one hidden layers is constructed, and the centerline obtained through feature extraction of the network can effectively overcome the influence of noise interference. Compared with the gray-gravity method and the Steger algorithm, the method in this paper has the advantage of high accuracy and can meet the requirements for extracting the center of complex light strips.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuanqi Gui "Dense center prediction for centerline extraction based on BP network", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 130880N (7 March 2024); https://doi.org/10.1117/12.3000906

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available