Autors: Christoff, N. V., Manolova, A. H., Jorda, L., Mari, J.-L.
Title: Morphological Crater Classification via Convolutional Neural Network with Application on MOLA data
Keywords: 3D mesh, CNN, Crater classification, Mars

Abstract: The only approach for a surface age dating is the impact crater count. In order to facilitate this process, many automatic approaches have been proposed for the impact crater detection. However, the origin and the morphological features of those impact craters can influence the accurate crater count. In this article, we propose a novel approach for crater morphological classification. The developed method is based on a study of a 3D triangulated mesh of Mars' sample. We use a curvature analysis and local quantization method in combination with a convolution neural network to automatically classify impact craters in three categories: valid, secondary and degraded craters.



    ANNA 2018 - Advances in Neural Networks and Applications 2018, pp. 96-100, 2018, Bulgaria, ISBN 978-3-8007-4756-6

    Цитирания (Citation/s):
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    2. Alnaim, N., Abbod, M., Swash, R., Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks, Technologies 2020, 8(2), 19;, 2020 - 2020 - в издания, индексирани в Scopus или Web of Science
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    4. Aji, S., Kumam, P., Siricharoen, P., Bukar, A. M., & Adamu, M. S. (2021). Deep Transfer Learning for Automated Artillery Crater Classification. Thai Journal of Mathematics, 19(3), 1068-1081. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus