Aggregate particle image segmentation

W. X. Wang

doi:10.1117/12.783690

27 November 2007 Aggregate particle image segmentation

W. X. Wang

Proceedings Volume 6723, 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment; 67234X (2007) https://doi.org/10.1117/12.783690
Event: 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes, 2007, Chengdu, China

Abstract

The most important and hard part of computer vision for aggregates, is segmentation. Segmentation can be divided into two steps, one is segmentation based on gray levels (called image binarization sometimes) in which a gray level image is processed and converted into a binary image, the other is segmentation based on particle shapes in a binary image, in which overlapping and touching particles will be split, and over-segmented particles will be merged based on some prior knowledge such as shape and size etc. In this paper, according to authors' work experiences, several kinds of aggregate image segmentation algorithms are analyzed and discussed; therefore, the suggestions for aggregate image segmentation are proposed.

Citation Download Citation

W. X. Wang "Aggregate particle image segmentation", Proc. SPIE 6723, 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 67234X (27 November 2007); https://doi.org/10.1117/12.783690

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