1. Barmpoutis, P., Dimitropoulos, K., Grammalidis, N., 2014. Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition. In: Proceedings of the 22nd European Signal Processing Conference, pp. 1078–1082.
2. Barmpoutis, P., Lefakis, P., 2016. Development of mathematical model for automatic recognition of Greek wood species (in Greek with English abstract). In: Proceedings of the 8th International Week Dedicated to Maths 2016, Hellenic Mathematical Society, Thessaloniki, pp. 241–251.
3. Bremananth, R., Nithya, B., Saipriya, R., 2009. Wood Species Recognition Using GLCM and Correlation. In: Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 615–619.
4. Bond, B., Hamner, P., 2002. Wood identification for hardwood and softwood species native to Tennessee. University of Tennessee Extension, Knoxville, USA. [online 24 November 2016] URL: .
5. Cavalin, P.R., Kapp, M.N., Martins, J., Oliveira, L.E., 2013. A multiple feature vector framework for forest species recognition. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 16–20. http://doi.org/10.1145/2480362.2480368.