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dc.contributor.authorCaglayan, A. and Can, A.B.
dc.date.accessioned2021-04-08T12:07:07Z
dc.date.available2021-04-08T12:07:07Z
dc.date.issued2019
dc.identifier10.1007/978-3-030-11015-4_51
dc.identifier.issn03029743
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061720256&doi=10.1007%2f978-3-030-11015-4_51&partnerID=40&md5=410ef1caf670684660a05c027d54d016
dc.identifier.urihttp://acikerisim.bingol.edu.tr/handle/20.500.12898/4217
dc.description.abstractThis paper proposes an approach for RGB-D object recognition by integrating a CNN model with recursive neural networks. It first employs a pre-trained CNN model as the underlying feature extractor to get visual features at different layers for RGB and depth modalities. Then, a deep recursive model is applied to map these features into high-level representations. Finally, multi-level information is fused to produce a strong global representation of the entire object image. In order to utilize the CNN model trained on large-scale RGB datasets for depth domain, depth images are converted to a representation similar to RGB images. Experimental results on the Washington RGB-D Object dataset show that the proposed approach outperforms previous approaches. © Springer Nature Switzerland AG 2019.
dc.language.isoEnglish
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleExploiting multi-layer features using a CNN-RNN approach for RGB-D object recognition


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