|
Regular Research Article 24 Jul 2013 Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environmentH. Madokoro, M. Tsukada, and K. SatoRelated authors- Prediction of bed-leaving behaviors using piezoelectric non-restraining sensors (30 Apr 2013)
H. Madokoro, N. Shimoi, and K. Sato J. Sens. Sens. Syst., 2, 27-34, 2013 - Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT (06 Aug 2013)
H. Madokoro, A. Yamanashi, and K. Sato Pattern Recogn. Phys., 1, 93-103, 2013
- Prediction of bed-leaving behaviors using piezoelectric non-restraining sensors (30 Apr 2013)
H. Madokoro, N. Shimoi, and K. Sato J. Sens. Sens. Syst., 2, 27-34, 2013 - Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT (06 Aug 2013)
H. Madokoro, A. Yamanashi, and K. Sato Pattern Recogn. Phys., 1, 93-103, 2013
Cited articles- Barnard, K., Duygulu, P., Freitas, N., Forsyth, D., Blei, D., and Jordan, M.: Matching Words and Pictures, J. Mach. Learn. Res., 3, 1107–1135, 2003.
- Biederman, I.: Human image understanding: recent research and a theory, The second workshop on Human and Machine Vision II, 13, 13–57, 1986.
- Carpenter, G. and Grossberg, S.: ART 2: Stable Self-Organization of Pattern Recognition Codes for Analog Input Patterns, Appl. Optics, 26, 4919–4930, 1987.
- Chen, Y., Zhu, L., Yuille, A., and Zhang, H.: Unsupervised Learning of Probabilistic Object Models(POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation, IEEE T. Pattern Anal. 31, 1747–1761, 2009.
- Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C.: Visual categorization with bags of keypoints, Proc. European Conf. Computer Vision, 59–74, 2004.
- Cummins, M. and Newman, P.: FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance, Int. J. Robot. Res., 27, 647–665, 2008.
- Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H., and Csorba, M.: An experimental and theoretical investigation into simultaneous localisation and map building (SLAM), Lecture Notes in Control and Information Sciences, Experimental Robotics VI, Springer, 2000.
- Fujita, Y.: Personal Robot R100, Journal of Robotics Society of Japan, 18, 40–41, 2000.
- Griffin, G., Holub, A., and Perona, P.: Caltech-256 Object Category Dataset, California Institute of Technology Technical Report, March 2007.
- Grossberg, S.: Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions, Biol. Cybern., 23, 187–202, 1976.
- Kohonen, T: Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59–69, 1982.
- Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
- Lempert, C., Blaschko, M., and Hofmann, T.: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search, Proc. Conf. Computer Vision and Pattern Recognition, 1–8, 2008.
- Lowe, D.: Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, 60, 91–110, 2004.
- Madokoro, H., Tsukada, M., and Sato, K.: Unsupervised Feature Selection and Category Formation for Generic Object Recognition, Proc. 14th International Conference on Computer Analysis of Images and Patterns, 427–434, 2011a.
- Madokoro, H., Tsukada, M., and Sato, K.: Unsupervised Feature Selection and Category Formation for Mobile Robot Vision, Proc. IEEE International Joint Conference on Neural Networks, 320–327, 2011b.
- Nakamura, T., Nagai, T., and Iwahashi, N.: Multimodal Object Categorization by a Robot, Journal of the Institute of Electronics, Information, and Communication Engineers D, J91–D, 10, 2507–2518, 2008.
- Nakano, K.: Manufacturing of a Brain: Thinking about Biotechnology from a Making of a Robot, Kyoritsu Shuppan, 1995.
- Nielsen, R.: Counterpropagation Networks, Appl. Optics, 26, 4979–4984, 1987.
- Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., and Williamson, R.: Estimating the Support of a High Dimensional Distribution, Neural Comput., 13, 1443–1471, 2001.
- Sivic, J., Russell, B., Efros, A., Zisserman, A., and Freeman, W.: Discovering Objects and their Localization in Images, Proc. Conf. Computer Vision, 370–377, 2005.
- Suzuki, K., Matsukawa, T., and Kurita, T.: Bag-of-features car detection based on selected local features using Support Vector Machine, Technical report of IEICE, Pattern Recognition and Media Understanding, 108, 7–12, 2009.
- Terashima, M., Shiratani, F., and Yamamoto, K.: Unsupervised Cluster Segmentation Method Using Data Density Histogram on Self-Organizing Feature Map, Journal of the Institute of Electronics, Information, and Communication Engineers (D–II), J79-D-II, 7, 1280–1290, 1996.
- Todorovic, S. and Ahuja, N.: Unsupervised Category, Modeling, Recognition, and Segmentation in Images, IEEE T. Pattern Anal., 30, 2158–2174, 2008.
- Tsukada, M., Madokoro, H., and Sato, K.: Unsupervised and Adaptive Category Classification for a Vision-Based Mobile Robot, Proc. IEEE World Congress on Computational Intelligence (WCCI), 4157–4162, July 2010.
- Tsukada, M., Utsumi, Y., Madokoro, H., and Sato, K.: Unsupervised Feature Selection and Category Classification for a Vision-Based Mobile Robot, IEICE Trans. Inf. & Sys., E94-D, 1, 127–136, 2011.
- Yanai, K.: The Current State and Future Directions on Generic Object Recognition. Journal of Information Processing, The Computer Vision and Image Media, 48, 1–24, 2007.
- Zhu, L., Chen, Y., and Yuille, A.: Unsupervised Learning of Probabilistic Grammar – Markov Models for Object Categories, IEEE Trans. Pattern Anal., 31, 114–128, 2009.
- More Articles (23)
|
|