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基于卷积神经网络的简单几何体三维模型自动分类识别研究

Research on Automatic Classification and Recognition of Simple Geometric 3D Models Based on Convolutional Neural Network
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摘要 本文针对五类简单几何体三维模型,设计了完整的卷积神经网络模型以实现自动分类识别。首先本文运用CATIA二次开发技术完成了模型的生产并收集了原始数据,其次运用图像处理的理论对原始数据进行了预处理,然后运用卷积神经网络理论完成了本文的卷积神经网络模型设计,最后进行了实验分析并验证了本文模型的可行性。 In this paper,a complete convolution neural network model is designed for automatic classification and recognition of five kinds of simple geometric three-dimensional models.Firstly,the CATIA secondary development technology is used to complete the production of the model and collect the original data.Secondly,the theory of image processing is used to preprocess the original data.Thirdly,the convolutional neural network model is designed by using the convolutional neural network theory.Finally,the feasibility of the model is analyzed and verified through experiments.
作者 党应聪 陈劲杰 DANG Yingcong;CHEN Jinjie(University of Shanghai for Science and Technology,Shanghai 200093,China)
机构地区 上海理工大学
出处 《软件工程》 2019年第4期13-16,共4页 Software Engineering
关键词 卷积神经网络 识别与分类 简单几何体 三维模型 convolutional neural network classification and recognition simple geometric 3D models
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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  • 1LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
  • 2HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554.
  • 3LEE H, GROSSE R, RANGANATH R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations [C]// ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning. New York: ACM, 2009: 609-616.
  • 4HUANG G B, LEE H, ERIK G. Learning hierarchical representations for face verification with convolutional deep belief networks [C]// CVPR '12: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2012: 2518-2525.
  • 5KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [C]// Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2012: 1106-1114.
  • 6GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2014: 580-587.
  • 7LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 3431-3440.
  • 8SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. [2015-11-04]. http://www.robots.ox.ac.uk:5000/~vgg/publications/2015/Simonyan15/simonyan15.pdf.
  • 9SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 1-8.
  • 10HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [EB/OL]. [2016-01-04]. https://www.researchgate.net/publication/286512696_Deep_Residual_Learning_for_Image_Recognition.

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软件工程
2019年 第4期

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