VoxRec: Hybrid convolutional neural network for active 3D object recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Karambakhsh A. | - |
dc.contributor.author | Sheng B. | - |
dc.contributor.author | Li P. | - |
dc.contributor.author | Yang P. | - |
dc.contributor.author | Jung Y. | - |
dc.contributor.author | Feng D.D. | - |
dc.date.available | 2020-05-25T03:36:26Z | - |
dc.date.created | 2020-05-12 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/45702 | - |
dc.description.abstract | Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and time-consuming training. In this paper, an innovative approach has been suggested for recognizing 3D models. It contains encoding 3D point clouds, surface normal, and surface curvature, merge them to provide more effective input data, and train it via a deep convolutional neural network on Shapenetcore dataset. We also proposed a similar method for 3D segmentation using Octree coding method. Finally, comparing the accuracy with some of the state-of-the-art demonstrates the effectiveness of our proposed method. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | VoxRec: Hybrid convolutional neural network for active 3D object recognition | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000530809000022 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2987177 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.70969 - 70980 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85084175821 | - |
dc.citation.endPage | 70980 | - |
dc.citation.startPage | 70969 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Jung Y. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | multi-layer neural network | - |
dc.subject.keywordAuthor | Object recognition | - |
dc.subject.keywordAuthor | octrees | - |
dc.subject.keywordAuthor | recurrent neural networks | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Object recognition | - |
dc.subject.keywordPlus | 3d object recognition | - |
dc.subject.keywordPlus | 3D segmentation | - |
dc.subject.keywordPlus | Conventional methods | - |
dc.subject.keywordPlus | Effective inputs | - |
dc.subject.keywordPlus | Innovative approaches | - |
dc.subject.keywordPlus | Neural network method | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Surface curvatures | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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