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Exploiting Multi-layer Features Using a CNN-RNN Approach for RGB-D Object Recognition.

A. Caglayan, и A. Can. ECCV Workshops (3), том 11131 из Lecture Notes in Computer Science, стр. 675-688. Springer, (2018)

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Ahmet Can

Ahmet Can Ülger

Mendel Burak

Antje Burak

Hikmet Ahmet

Другие публикации лиц с тем же именем

A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods.O. Guclu, и A. Can. ICIAR, том 9164 из Lecture Notes in Computer Science, стр. 297-305. Springer, (2015)Action recognition with skeletal volume and deep learning.A. Keçeli, A. Kaya, и A. Can. SIU, стр. 1-4. IEEE, (2017)Automatic Object Segmentation on RGB-D Data using Surface Normals and Region Similarity.H. Yalic, и A. Can. VISIGRAPP (4: VISAPP), стр. 379-386. SciTePress, (2018)Histogram Based Visual Place Recognition for Improving SLAM Performance.O. Guclu, и A. Can. ICARSC, стр. 174-180. IEEE, (2016)Systematic Approach for Generation of Feasible Deployment Alternatives for Microservices.I. Aksakalli, T. Çelik, A. Can, и B. Tekinerdogan. IEEE Access, (2021)A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.A. Kaya, и A. Can. J. Biomed. Informatics, (2015)GenTrust: A genetic trust management model for peer-to-peer systems.U. Tahta, S. Sen, и A. Can. Appl. Soft Comput., (2015)Automatic recognition of traffic signs.H. Yalic, и A. Can. ISPA, стр. 361-366. IEEE, (2011)Low Latency Action Recognition with Depth Information.A. Keçeli, и A. Can. VISIGRAPP (4: VISAPP), стр. 590-596. SciTePress, (2016)When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition.A. Caglayan, N. Imamoglu, A. Can, и R. Nakamura. Comput. Vis. Image Underst., (2022)

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