An overview of traditional and deep-learning-based image segmentation methods

Mengxuan Li; Jun Luo; Xiangning Li; Yue Zhao

doi:10.1117/12.3035203

19 July 2024 An overview of traditional and deep-learning-based image segmentation methods

Mengxuan Li, Jun Luo, Xiangning Li, Yue Zhao

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130H (2024) https://doi.org/10.1117/12.3035203
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Image segmentation remains a challenging problem in computer vision. Various segmentation methods have been developed, including traditional methods based on threshold, edge, region, and morphology, as well as novel methods based on deep learning. The segmentation precision is continuously improving, and its applications are expanding. Starting with traditional image segmentation methods, we organized their performance and practical application in relation to the demand for segmentation of IC surface defects. We then tested the segmentation of the IC surface defects database we created. However, the final result was unsatisfactory. Therefore, we are continuing to explore segmentation models based on deep learning and summarise their performance evaluation to explore the possibility of applying them to the segmentation of IC surface defects.

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

Citation Download Citation

Mengxuan Li, Jun Luo, Xiangning Li, and Yue Zhao "An overview of traditional and deep-learning-based image segmentation methods", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130H (19 July 2024); https://doi.org/10.1117/12.3035203

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