In this paper, we validate crater detection in moon surface image using FLDA. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) project aiming at the pin-point landing to the moon surface. The point where the lander should land is judged by the position relations of the craters obtained via camera, so the real-time image processing becomes important element. Besides, in the SLIM project, 400kg-class lander is assumed, therefore, high-performance computers for image processing cannot be equipped. We are studying various crater detection methods such as Haar-Like features, LBP, and PCA. And we think these methods are appropriate to the project, however, to identify the unlearned images obtained by actual is insufficient. In this paper, we examine the crater detection using FLDA, and compare with the conventional methods.
[1] Yoshikawa S., Kunugi M., Yasumitsu R., Sawai S., Fukuda S., Mizuno T., Nakaya K., Fujii Y. Takatsuka N. "Conceptual Study on the Guidance, Navigation and Control System of the Smart Landing for Investigating Moon (SLIM)", Proc. of Global Lunar Conference 2010, paper ID 5644 (2010).
[2] P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features, Proc. CVPR, Vol. 1, pp.511-518, 2001.H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
[3] Matti Pietikainen, Guoying Zhao, Abdenour Hadid, Timo Ahonen,"Computer Vision Using Local Binary Patterns," Springer-Verlag, 2011.
[4] H. Schneiderman, "Feature-Centric Evaluation for Efficient Cascaded Object Detection," Proc. CVPR, Vol. 2, pp.29-36, 2004. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
[5] S. Mizumi, H. Kamata, K. Takadama, "Study on crater dection system using Haar-Like features in Japanese," 54h Proceedings of the Space Sciences and Technology Conference, paper ID 2A13, 2010.
[6] T. Tanaami, Y. Takeda, N. Aoyama, S. Mizumi, H. Kamata, K. Takadama, S. Ozawa, S. Fukuda and S. Sawai, "Crater Detection Using Haar-Like Feature for Moon Landing System Based on the Surface Image", Proc. 28th International Symposium on Space Technology and Science, 2011-d, 37, 2011.
[7] M. S. Bartlett, J. R. Movellan and T. J. Sejnowski, "Face Recognition by Independent Component Analysis”, Trans. IEEE, Vol. 13, No. 6, pp.1450-1464, 2002.
[8] Y. Takeda, T. Tanaami, N. Aoyama, S. Mizumi and H. Kamata, "Study on preprocessing and postprocessing to improve crater deteciton performance in Japanese," 55th Proceedings of the Space Sciences and Technology Conference, paper ID 3H08, 2011.
[9] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class-specific linear projection", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.
[10] R. A. Fisher, "The Use of Multiple Treasures in Taxonomic Problems", Ann. Eugenics, vol. 7, pp. 179-188, 1936.
[11] Richard O. Duda, Peter E. Hart,, David G. Stork, "Pattern Classification (2nd.Ed.)", john Wiley & Sons, Inc.(2001), pp.117-120.
[1] Yoshikawa S., Kunugi M., Yasumitsu R., Sawai S., Fukuda S., Mizuno T., Nakaya K., Fujii Y. Takatsuka N. "Conceptual Study on the Guidance, Navigation and Control System of the Smart Landing for Investigating Moon (SLIM)", Proc. of Global Lunar Conference 2010, paper ID 5644 (2010).
[2] P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features, Proc. CVPR, Vol. 1, pp.511-518, 2001.H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
[3] Matti Pietikainen, Guoying Zhao, Abdenour Hadid, Timo Ahonen,"Computer Vision Using Local Binary Patterns," Springer-Verlag, 2011.
[4] H. Schneiderman, "Feature-Centric Evaluation for Efficient Cascaded Object Detection," Proc. CVPR, Vol. 2, pp.29-36, 2004. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
[5] S. Mizumi, H. Kamata, K. Takadama, "Study on crater dection system using Haar-Like features in Japanese," 54h Proceedings of the Space Sciences and Technology Conference, paper ID 2A13, 2010.
[6] T. Tanaami, Y. Takeda, N. Aoyama, S. Mizumi, H. Kamata, K. Takadama, S. Ozawa, S. Fukuda and S. Sawai, "Crater Detection Using Haar-Like Feature for Moon Landing System Based on the Surface Image", Proc. 28th International Symposium on Space Technology and Science, 2011-d, 37, 2011.
[7] M. S. Bartlett, J. R. Movellan and T. J. Sejnowski, "Face Recognition by Independent Component Analysis”, Trans. IEEE, Vol. 13, No. 6, pp.1450-1464, 2002.
[8] Y. Takeda, T. Tanaami, N. Aoyama, S. Mizumi and H. Kamata, "Study on preprocessing and postprocessing to improve crater deteciton performance in Japanese," 55th Proceedings of the Space Sciences and Technology Conference, paper ID 3H08, 2011.
[9] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class-specific linear projection", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997.
[10] R. A. Fisher, "The Use of Multiple Treasures in Taxonomic Problems", Ann. Eugenics, vol. 7, pp. 179-188, 1936.
[11] Richard O. Duda, Peter E. Hart,, David G. Stork, "Pattern Classification (2nd.Ed.)", john Wiley & Sons, Inc.(2001), pp.117-120.
@article{"International Journal of Information, Control and Computer Sciences:64700", author = "Yoshiaki Takeda and Norifumi Aoyama and Takahiro Tanaami and Syouhei Honda and Kenta Tabata and Hiroyuki Kamata", title = "Study on Crater Detection Using FLDA", abstract = "In this paper, we validate crater detection in moon surface image using FLDA. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) project aiming at the pin-point landing to the moon surface. The point where the lander should land is judged by the position relations of the craters obtained via camera, so the real-time image processing becomes important element. Besides, in the SLIM project, 400kg-class lander is assumed, therefore, high-performance computers for image processing cannot be equipped. We are studying various crater detection methods such as Haar-Like features, LBP, and PCA. And we think these methods are appropriate to the project, however, to identify the unlearned images obtained by actual is insufficient. In this paper, we examine the crater detection using FLDA, and compare with the conventional methods.
", keywords = "Crater Detection, Fisher Linear Discriminant
Analysis , Haar-Like Feature, Image Processing.", volume = "6", number = "11", pages = "1544-5", }
