Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

Sebastian Budzan

doi:10.1117/12.2309765

13 April 2018 Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

Sebastian Budzan

Author Affiliations +

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960B (2018) https://doi.org/10.1117/12.2309765
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.

Citation Download Citation

Sebastian Budzan "Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960B (13 April 2018); https://doi.org/10.1117/12.2309765

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