Segmentation Method Overview for Thermal Images in Matlab Computational Environment

  • Ondrej Bostik Department of Control and Instrumentation, Brno University of Technology, Czech Republic https://orcid.org/0000-0002-7856-2084
  • Sobeslav Valach Department of Control and Instrumentation, Brno University of Technology, Czech Republic
  • Karel Horak Department of Control and Instrumentation, Brno University of Technology, Czech Republic
  • Jan Klecka Department of Control and Instrumentation, Brno University of Technology, Czech Republic
Keywords: MATLAB, segmentation, thermal images, dataset, Otsu's segmentation, adaptive thresholding, k-means clustering, active contour

Abstract

This paper presents an overview of methods usable for segmentation of thermal images in MATLAB computational environment. The goal of this work is the demonstration usage of available methods and evaluate their performance. Part of the work is to present the datasets we create for testing. This paper is part of our ongoing work focused on segmentation of thermal images from the process of traverse wedge rolling.

References

Arthur, D. and Vassilvitskii, S. 2007. K-Means++: The Advantages of Careful Seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA. SIAM, pp. 1027-1035. DOI: 10.1145/1283383.1283494

Blohm, T., Langner, J., Stonis, M, Behrens, B.-A. 2017. Basic study of incremental forming of serially arranged hybrid parts using cross-wedge rolling. Procedia Eng. 207, Jan, pp. 1677-1682. DOI: 10.1016/J.PROENG.2017.10.921

Bradley, D. and Roth, G. 2007. Adaptive Thresholding using the Integral Image. J. Graph. Tools 12, pp. 13-21. DOI: 10.1080/2151237X.2007.10129236

Chan, T. F. and Vese, L. A. 2001. Active contours without edges. IEEE Trans. Image Process 10, 2, pp. 266-277. DOI: 10.1109/83.902291

Crow, F. C. 1984. Summed-area Tables for Texture Mapping. In Proc. 11th Annu. Conf. Comput. Graph. Interact. Tech.. ACM, pp. 207-212. DOI: 10.1145/800031.808600

Fu, X. P. and Dean, T. A. 1993. Past developments, current applications and trends in the cross wedge rolling process. Int. J. Mach. Tools Manuf. 33, 3, pp. 367-400. DOI: 10.1016/0890-6955(93)90047-X

Kaur, D. and Kaur, Y. 2014. Various image segmentation techniques: a review. Int. J. Comput. Sci. Mob. Comput. 3, 4, pp. 809-814.

Najman, L. and Talbot, H. 2013. Mathematical Morphology: From Theory to Applications. John Wiley & Sons.

Otsu, N. 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man. Cybern. 9, 1, pp. 62-66. DOI: 10.1109/TSMC.1979.4310076

Pater, Z., Tomczak, J, and Bulzak, T. 2018. New forming possibilities in cross wedge rolling processes. wArch. Civ. Mech. Eng. 18, 1, pp. 149-161. DOI: 10.1016/J.ACME.2017.06.005

Protiere, A. and Sapiro, G. 2007. Interactive Image Segmentation via Adaptive Weighted Distances. IEEE Trans. Image Process. 16, 4, pp. 1046-1057. DOI: 10.1109/TIP.2007.891796

Stehman, S. V. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62, 1, pp. 77-89. DOI: 10.1016/S0034-4257(97)00083-7

Whitaker, R. T. 1998. A Level-Set Approach to 3D Reconstruction from Range Data. Int. J. Comput. Vis. 29, 3, pp. 203-231. DOI: 10.1023/A:1008036829907

Published
2019-06-24
How to Cite
[1]
Bostik, O., Valach, S., Horak, K. and Klecka, J. 2019. Segmentation Method Overview for Thermal Images in Matlab Computational Environment. MENDEL. 25, 1 (Jun. 2019), 43-50. DOI:https://doi.org/10.13164/mendel.2019.1.043.
Section
Articles