References

Axelsson, J. and J. Sorensen, 2013. The 2D Hotelling filter-a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction. BMC. Med. Phys., Vol. 13, 10.1186/1756-6649-13-1

Elmer, P., A. Lupp, S. Sprenger, R. Thaler and A. Uhl, 2015. Exploring compression impact on face detection using haar-like features. In: Proceedings of the19th Scandinavian Conference on Image Analysis (SCIA 2015), June 15-17, 2015, Springer, Copenhagen, Denmark, pp: 53-64.

Georgakopoulos, S.V., S.K. Tasoulis, V.P. Plagianakos and I. Maglogiannis, 2013. Artificial neural networks and principal components analysis for detection of idiopathic pulmonary fibrosis in microscopy images. Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN 2013), September 13-16, 2013, Springer, Berlin, Germany, pp: 292-301.

Gomes S.L., E.D.S. Reboucas, E.C. Neto, J.P. Papa and V.H.C. Albuquerque et al., 2016. Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput. Appl., 1: 1-12.
CrossRef  |  Direct Link  |  

Halfawy, M.R. and J. Hengmeechai, 2014. Integrated vision-based system for automated defect detection in sewer closed circuit television inspection videos. J. Comput. Civil Eng., Vol. 29,

Kolsch, M. and S. Butner, 2009. Hardware Considerations for Embedded Vision Systems. In: Embedded Computer Vision, Kisacanin, B., S.S. Bhattacharyya and C. Sek (Eds.). Springer, London, England, ISBN:978-1-84800-303-3, pp: 3-26.

Liu, Z., H. Ukida, K. Niel and P. Ramuhalli, 2015. Industrial Inspection with Open Eyes: Advance with Machine Vision Technology. In: Integrated Imaging and Vision Techniques for Industrial Inspection, Zheng, L., U. Hiroyuki, R. Pradeep and N. Kurt (Eds.). Springer, London, England, ISBN:978-1-4471-6740-2, pp: 1-37.

Nazarbakhsh, B. and A.A. Manaf, 2014. Image Pre-processing Techniques for Enhancing the Performance of Real-Time Face Recognition System Using PCA. In: Bio-inspiring Cyber Security and Cloud Services; Trends and Innovations, Hassanien, A.E., T.H. Kim, K. Janusz and A.I. Awad (Eds.). Springer, Berlin, Germany, ISBN:978-3-662-43615-8, pp: 383-422.

Piotr, P., R. Doroz and T. Orczyk, 2015. The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition. Pattern Anal. Appl., 18: 983-1001.
CrossRef  |  Direct Link  |  

Smithsonian Institution, 2016. Eruptions, earthquakes and emissions. Smithsonian Institution, Washington, D.C., USA. http://volcano.si.edu/E3/