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First feedback indicates that the method for segmentation and pool profile generation is applicable for a wide range of steel products. This might require further implementations and/or parametrization for segmentation and pool profile generation. In the future, as image acquisition will take place regularly and, thus, more data will be available, we intend to investigate approaches based on deep learning, that will enhance automated segmentation and quality assessment even further. ACKNOWLEDGMENT This research was partly funded by BMVIT/BMWFJ under COMET programme, project nr. 836630, by ”Land Steiermark” trough SFG under project nr. 1000033937, and by the ’Vienna Business Agency’. REFERENCES [1] A. Ahmadvand and M. R. Daliri, “Invariant texture classification using a spatial filter bank in multi-resolution analysis,” Image and Vision Computing, vol. 45, pp. 1 – 10, 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0262885615001328 [2] S. I. Chang and J. S. Ravathur, “Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology,” Quality Engineering, vol. 17, no. 3, pp. 435–451, 2005. [Online]. Available: http://dx.doi.org/10.1081/QEN- 200059881 [3] B. L. DeCost and E. A. Holm, “A computer vision approach for automated analysis and classification of microstructural image data,” Computational Materials Science, vol. 110, pp. 126 – 133, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0927025615005066 [4] L. Hong, Y. Wan, and A. Jain, “Fingerprint image enhancement: algorithm and performance evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777–789, Aug 1998. [5] N. Neogi, D. K. Mohanta, and P. K. Dutta, “Review of vision-based steel surface inspection systems,” EURASIP Journal on Image and Video Processing, vol. 2014, no. 1, p. 50, 2014. [Online]. Available: http://dx.doi.org/10.1186/1687-5281-2014-50 [6] V.S.N.PrasadandJ.Domke,“Gaborfiltervisualization,” inTechnical Report. Maryland: University of Maryland, 2005. [7] A. Rinnhofer, W. Benesova, G. Jakob, and M. Stockinger, “Feature extraction from micrographs of forged nickel based alloy,” in 18th International Conference on Pattern Recognition (ICPR’06), vol. 2, 2006, pp. 391–394. [8] W. Schu¨tzenho¨fer, G. Reiter, R. Tanzer, H. Scholz, R. Sorci, F. Arcobello-Varlese, and A. Carosi, “Experimental investigations for the validation of a numerical pesr model,” in International Symposium on Liquid Metal Processing and Casting. Nancy, France: SF2M, 2007, pp. 49–55. [9] D. M. Stefanescu and R. Ruxanda, ASM Handbook Volume 9: Metallography and Microstructures. ASM International, 2004, ch. Fundamentals of Solidification, pp. 71–92. [10] M. T., “The local binary pattern approach to texture analysis - extensions and applications.” Ph.D. dissertation, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, 2003, dissertation. Acta Univ Oul C 187, 78 p + App. [Online]. Available: http://herkules.oulu.fi/isbn9514270762/ [11] R. Tanzer, A. Graf, W. Schu¨tzenho¨fer, and G. Reiter, “Description and validation of a var-model for a high strength maraging steel,” in 2nd International Conference on Modelling of Metallurgical Processes, Graz, Austria, 2007. 127