Feature Extraction and Image Recognition with Convolutional Neural Networks
The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by...
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| Published in | Journal of physics. Conference series Vol. 1087; no. 6; pp. 62032 - 62038 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.09.2018
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| Online Access | Get full text |
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| Abstract | The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented. |
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| AbstractList | The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented. |
| Author | Liu, Yu Han |
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| Cites_doi | 10.1109/IJCNN.1989.118638 10.1109/5.726791 10.1109/29.21701 |
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| References | Jain A. K. (1) 2011 4 5 LeCun Y. (10) 2013 Glorot X (9) 2011; 130 7 Koushik J. (3) 2016 Theano Development Team (6) Szeliski R. (2) 2010 Jarrett K (8) 2010; 30 |
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| SubjectTerms | Algorithms Artificial neural networks Feature extraction Feature recognition Neural networks Object recognition Physics Structural hierarchy Taste |
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| Title | Feature Extraction and Image Recognition with Convolutional Neural Networks |
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