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Citation - Scopus: 3
Derin Öǧrenme ile Zemin Dokusu Sınıflandırma
(IEEE, 2018) Ozuysal, Mustafa
In this study, we investigate the use of transfer learning on various deep neural network architectures pretained on the ImageNet data set for ground texture classification purposes. We introduce a new ground texture data set collected from seven different areas. We retrain deep neural network's last layer or when possible the full set of layers on this data set. The results show that it is possible to discriminate the ground textures even when very small images are used.
Robust Keypoint Matching for Three Dimensional Scenes and Object Recognition
(IEEE, 2017) Koksal, Ali; Uzyildirim, Furkan Eren; Ozuysal, Mustafa
In this paper, we adapt a recently proposed keypoint matching approach for binary descriptors and planar objects to three dimensional objects. We also evaluate the performance of this approach for a museum object recognition application containing more than one hundred paintings. Moreover, we quantify the effect of selecting only descriptors with high matching ratio on the success rate of the object recognition application.

Computer Engineering / Bilgisayar Mühendisliği