<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic content evaluation. Automatically prepared solutions, based on machine learning, constitute an effective and sufficiently precise tool for classification. Human-developed algorithms, on the other hand, require much more experience in preparation, but allow better control of factors affecting the final result. Both attempts were described and compared.</p></abstract>

Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic content evaluation. Automatically prepared solutions, based on machine learning, constitute an effective and sufficiently precise tool for classification. Human-developed algorithms, on the other hand, require much more experience in preparation, but allow better control of factors affecting the final result. Both attempts were described and compared.

Machine Learning Versus Human-Developed Algorithms in Image Analysis of Microstructures

Conference Quality Production Improvement – CQPI

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic...

Machine Learning Versus Human-Developed Algorithms in Image Analysis of Microstructures

Published Online: Oct 08, 2019

Page range: 412 - 416

Received: Apr 08, 2019

Accepted: May 22, 2019

DOI: https://doi.org/10.2478/cqpi-2019-0056

Keywords
image analysis, object detection, neural networks, machine learning

© 2019 Adam Piwowarczyk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Machine Learning Versus Human-Developed Algorithms in Image Analysis of Microstructures

Published Online: Oct 08, 2019

Page range: 412 - 416

Received: Apr 08, 2019

Accepted: May 22, 2019

DOI: https://doi.org/10.2478/cqpi-2019-0056

Keywordsimage analysis, object detection, neural networks, machine learning

© 2019 Adam Piwowarczyk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
image analysis, object detection, neural networks, machine learning