Convolutional neural networks applied to house numbers digit classification

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2012, Pages 3288-3291

  Sermanet, Pierre ^a   Chintala, Soumith ^a   Lecun, Yann ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICALLY INSPIRED; CONVNET; CONVOLUTIONAL NEURAL NETWORK; DIGIT CLASSIFICATION; HIERARCHICAL FEATURES; MULTI-STAGE; REAL-WORLD; SOURCE CODES;

PATTERN RECOGNITION;

NEURAL NETWORKS;

EID: 84874575248     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Y. Boureau, J. Ponce, and Y. LeCun. A theoretical analysis of feature pooling in vision algorithms. In Proc. International Conference on Machine learning, 2010.
2. D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber. A committee of neural networks for traffic sign classification. In International Joint Conference on Neural Networks, pages 1918-1921, 2011.
3. T. E. de Campos, B. R. Babu, and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, February 2009.
4. J. Fan, W. Xu, Y. Wu, and Y. Gong. Human tracking using convolutional neural networks. Neural Networks, IEEE Transactions on, 21(10):1610-1623, 2010.
5. A. Hyvrinen and U. Kster. Complex cell pooling and the statistics of natural images. In Computation in Neural Systems., 2005.
6. K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. Le-Cun. Learning invariant features through topographic filter maps. In Proc. International Conference on Computer Vision and Pattern Recognition. IEEE, 2009.
7. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
8. Y. Lecun and C. Cortes. The MNIST database of handwritten digits.
9. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
10. P. Sermanet, K. Kavukcuoglu, and Y. LeCun. Traffic signs and pedestrians vision with multi-scale convolutional networks. In Snowbird Machine Learning Workshop, 2011.
11. P. Sermanet, K. Kavukcuoglu, and Y. LeCun. Eblearn: Open-source energy-based learning in c. In Proc. International Conference on Tools with Artificial Intelligence. IEEE, 2009.
12. P. Sermanet and Y. LeCun. Traffic sign recognition with multi-scale convolutional networks. In Proceedings of International Joint Conference on Neural Networks, 2011.
13. E. P. Simoncelli and D. J. Heeger. A model of neuronal responses in visual area mt, 1997.
14. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In IEEE International Joint Conference on Neural Networks, pages 1453-1460, 2011.
15. T. Yamaguchi, Y. Nakano, M. Maruyama, H. Miyao, and T. Hananoi. Digit classification on signboards for telephone number recognition. In ICDAR, pages 359-363, 2003.
16. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In in IEEE Conference on Computer Vision and Pattern Recognition, 2009.