Abstract

The growing industrialization has emphasized the need for high-performance computer-based inspection methods. Here, we investigated the performance of four major computer-based texture characterization methods in the prediction of visually perceived and actual surface coarseness of real materials. Gray level co-occurrence matrix (GLCM), distance-dependent edge frequency (DDEF), fractal dimension (FD), and histogram skewness (SK) were used as the methods. A novel collection of real materials consisting of 20 sandpapers with high, medium, and low coarseness levels was employed. The results revealed that at high coarseness level the most precise prediction of actual surface coarseness was made by GLCM and SK, while in the prediction of visual coarseness all the methods worked similarly effectively. Perfect correlations were observed between GLCM, FD, and SK at visual and also actual coarseness at medium coarseness level. At low coarseness level, SK and DDEF acceptably predicted visual and actual coarseness, respectively. The image resolution impact on performance of the computer-based methods was found to be substantial. Results of the research present a guideline for choosing the best computer-based method as a viable substitute for the human observer in online inspections of materials’ texture.

© 2018 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Nondestructive, fast, and cost-effective image processing method for roughness measurement of randomly rough metallic surfaces

Sajjad Ghodrati, Saeideh Gorji Kandi, and Mohsen Mohseni
J. Opt. Soc. Am. A 35(6) 998-1013 (2018)

Image statistics for surface reflectance perception

Lavanya Sharan, Yuanzhen Li, Isamu Motoyoshi, Shin'ya Nishida, and Edward H. Adelson
J. Opt. Soc. Am. A 25(4) 846-865 (2008)

Texture classification using discrete Tchebichef moments

J. Víctor Marcos and Gabriel Cristóbal
J. Opt. Soc. Am. A 30(8) 1580-1591 (2013)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (12)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (5)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (8)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription