Cited By
View all- Castellanos A(2019)Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and GrayscaleJournal of Physics: Conference Series10.1088/1742-6596/1229/1/0120421229(012042)Online publication date: 29-May-2019
Classification of occluded 2D objects using deep learning of 3D shape surfaces
Authors: 
Vasileios Tzitzilonis, Vassilios Kappatos, Evangelos Dermatas, George ApostolopoulosAuthors Info & Claims
Article No.: 57, Pages 1 - 2
Abstract
This paper presents a novel deep learning method for partially occluded 2D object classification. A 2D Convolutional Neural Network (CNN) was trained with partial and whole images of the 3D models obtained from different camera views. The efficiency of the proposed method in classifying partial objects in 40 categories is more than 80% in most objects and exceeds 95% in some of them.
References
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Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shapes. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., (June 2015) 1912--1920.
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V. Hegde and R. Zadeh, "FusionNet: 3D Object Classification Using Multiple Data Representations," Computer Vision and Pattern Recognition, arXiv:1607.05695 (2016).
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H. Su, S. Maji, E. Kalogerakis, and E. Learned-miller. 2015. Multi-view Convolutional Neural Networks for 3D Shape Recognition. Ieee Iccv, (2015) 945--953.
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G. Stavropoulos, P. Moschonas, K. Moustakas, D. Tzovaras, and M. G. Strintzis. 2010. 3-D Model Search and Retrieval From Range Images Using Salient Features. IEEE Trans. Multimed., 12, 7, (2010). 692--704.
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Index Terms
- Classification of occluded 2D objects using deep learning of 3D shape surfaces
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Information
Published In
July 2018
339 pages
ISBN:9781450364331
DOI:10.1145/3200947
Copyright © 2018 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
In-Cooperation
- EETN: Hellenic Artificial Intelligence Society
- UOP: University of Patras
- University of Thessaly: University of Thessaly, Volos, Greece
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 July 2018
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View all- Castellanos A(2019)Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and GrayscaleJournal of Physics: Conference Series10.1088/1742-6596/1229/1/0120421229(012042)Online publication date: 29-May-2019
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