1. J. G. Verly, R. L. Delanoy, and D. E. Dudgeon, “Machine Intelligence Technology for Automatic Target Recognition,”The Lincoln Laboratory Journal, vol. 2, no. 2, pp. 277–310, 1989.

   Google Scholar 

2. A. Arora, P. Dutta, S. Bapat, V. Kulathumani, H. Zhang, V. Naik, V. Mittal, H. Cao, M. Demirbas, M. Gouda, Y. Choi, T. Herman, S. Kulkarni, U. Arumugam, M. Nesterenko, A. Vora, and M. Miyashita, “A line in the sand: a wireless sensor network for target detection, classification, and tracking,”Computer Networks, vol. 46, no. 5, pp. 605–634, 2004.

   Google Scholar 

3. D. Kraus and A. M. Zoubir, “Contributions to Automatic Target Recognition Systems for Underwater Mine Classification,”IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 505–518, 2015.

   Article  Google Scholar 

4. S. G. Narasimhan and S. K. Nayar, “Shedding light on the weather,”2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003 Proceedings, vol. 1, 2003.

   Google Scholar 

5. S. K. Nayar and S. G. Narasimhan, “Vision in bad weather,”Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820–827, 1999.

   Article  Google Scholar 

6. J. M. Lloyd,Thermal imaging systems. Springer Science & Business Media, 2013.

   Google Scholar 

7. M. Vollmer, J. A. Shaw, and P. W. Nugent, “Visible and invisible mirages: comparing inferior mirages in the visible and thermal infrared,”in journal of applied optics, vol. 54, no. 4, pp. B76–B84, 2014.

   Article  Google Scholar 

8. Y. Fang, K. Yamada, Y. Ninomiya, B. Horn, and I. Masaki, “Comparison between infrared-image-based and visible-image-based approaches for pedestrian detection,”IEEE IV2003 Intelligent Vehicles Symposium Proceedings (Cat No03TH8683), pp. 505–510, 2003.

   Google Scholar 

9. A. Akula, R. Ghosh, S. Kumar, and H. K. Sardana, “Moving target detection in thermal infrared imagery using spatiotemporal information.,”Journal of the Optical Society of America A, Optics, image science, and vision, vol. 30, no. 8, pp. 1492–501, 2013.

   Article  Google Scholar 

10. M. Khayyat, L. Lam, and C. Y. Suen, “Learning-based word spotting system for Arabic handwritten documents,”Pattern Recognition, vol. 47, no. 3, pp. 1021–1030, 2014.

    Article  Google Scholar 

11. B. Li, W. Hu, W. Xiong, O. Wu, and W. Li, “Horror Image Recognition Based on Emotional Attention,” inAsian Conference on Computer Vision (ACCV), 2011, pp. 594–605.

    Google Scholar 

12. S. Z. Li, L. Zhang, S. Liao, X. X. Zhu, R. Chu, M. Ao, and H. Ran, “A Near-infrared Image Based Face Recognition System,”7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 455–460, 2006.

    Google Scholar 

13. V. Elangovan and A. Shirkhodaie, “Recognition of human activity characteristics based on state transitions modeling technique,” p. 83920 V–83920 V–10, May 2012.

    Google Scholar 

14. B. Li, R. Chellappa, R. Chellappa, Q. Zheng, Q. Zheng, S. Der, S. Der, N. Nasrabadi, N. Nasrabadi, L. Chan, L. Chan, L. Wang, and L. Wang, “Experimental evaluation of FLIR ATR approaches—A comparative study,”Computer Vision and Image Understanding, vol. 84, pp. 5–24, 2001.

    Article MATH  Google Scholar 

15. T. Ahonen, A. Hadid, M. Pietikäinen, S. S. Member, and M. Pietika, “Face description with local binary patterns: application to face recognition.,”IEEE transactions on pattern analysis and machine intelligence, vol. 28, no. 12, pp. 2037–41, 2006.

    Article  Google Scholar 

16. A. P. Psyllos, C. N. E. Anagnostopoulos, and E. Kayafas, “Vehicle logo recognition using a sift-based enhanced matching scheme,”IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 322–328, 2010.

    Article  Google Scholar 

17. O. Déniz, G. Bueno, J. Salido, and F. De La Torre, “Face Recognition Using Histograms of Oriented Gradients,”Pattern Recognition Letters, vol. 32, no. 12, pp. 1598–1603, 2011.

    Article  Google Scholar 

18. I. Arel, D. C. Rose, and T. P. Karnowski, “Deep Machine Learning — A New Frontier in Artificial Intelligence Research,”IEEE Computational Inteligence Magazine, vol. 5, no. November, pp. 13–18, 2010.

    Article  Google Scholar 

19. D. Ciresan, “Multi-column Deep Neural Networks for Image Classification,” inComputer Vision and Pattern Recognition, IEEE, 2012, pp. 3642–3649.

    Google Scholar 

20. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,”Advances In Neural Information Processing Systems, pp. 1–9, 2012.

    Google Scholar 

21. P. Sermanet, S. Chintala, and Y. LeCun, “Convolutional neural networks applied to house numbers digit classification,”Proceedings of International Conference on Pattern Recognition ICPR12, pp. 10–13, 2012.

    Google Scholar 

22. Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” inProceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004, vol. 2, pp. 97–104.

    Google Scholar 

23. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”Proceedings of the IEEE, vol. 86, pp. 2278–2323, 1998.

    Article  Google Scholar 

24. J. L. Chu and A. Krzy, “Analysis of Feature Maps Selection in Supervised Learning Using Convolutional Neural Networks,” inAdvances in Artificial Intelligence, Springer International Publishing, 2014, pp. 59–70.

    Google Scholar 

25. S. Karsoliya, “Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture,”International Journal of Engineering Trends and Technology, vol. 3, no. 6, pp. 714–717, 2012.

    Google Scholar 

26. A. Akula, N. Khanna, R. Ghosh, S. Kumar, A. Das, and H. K. Sardana, “Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences,”Infrared Physics & Technology, vol. 63, pp. 103–109, 2014.

    Article  Google Scholar 

27. S. Ioffe and C. Szegedy, “Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift,”arXiv preprintarXiv:150203167v3, 2015.

28. H. Yu and B. M. Wilamowski, “Levenberg-Marquardt training,”Industrial Electronics Handbook, vol 5—Intelligent Systems, pp. 12–1 to 12–18, 2011.

    Google Scholar 

Download references