|
Regular Research Article 06 Aug 2013 Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFTH. Madokoro, A. Yamanashi, 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 and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment (24 Jul 2013)
H. Madokoro, M. Tsukada, and K. Sato Pattern Recogn. Phys., 1, 63-74, 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 and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment (24 Jul 2013)
H. Madokoro, M. Tsukada, and K. Sato Pattern Recogn. Phys., 1, 63-74, 2013
Cited articles- Bay, H., Ess, A., Tuytelaars, T., and Gool, L.: SURF: Speeded Up Robust Features, Comput. Vis. Image Und., 110, 346–359, 2008.
- Bosch, A., Zisserman, A., and Munoz, X.: Scene Classification Using a Hybrid Generative Discriminative Approach, IEEE T. Pattern Anal., 30, 712–727, 2008.
- Carpenter, G. A. and Grossberg, S.: ART 2: Stable Self-Organization of Pattern Recognition Codes for Analog Input Patterns, Appl. Optics, 26, 4919–4930, 1987.
- Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F., and Csorba, M.: An experimental and theoretical investigation into simultaneous localization and map building (SLAM), Lecture Notes in Control and Information Sciences: Experimental Robotics VI, Springer, 2000.
- Honma, K., Madokokoro, H., and Sato, K.: Estimation of Interests and Classification of Behavior Patterns with Trajectory Analysis Used for Event Sites, The IEICE transactions on information and systems, J95-D 10, 1848–1858, 2012.
- Kanada, T., Hirano, T., Eaton, D., and Ishiguro, H.: Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial, Hum.-Comput. Interact., 19, 61–84, 2004.
- Katsura, H., Miura, J., Hild, M., and Shirai, Y.: A View-Based Outdoor Navigation Using Object Recognition Robust to Changes of Weather and Seasons, Proc. IEEE/RSJ Int'l Conf. Intelligent Robot and Systems, 2974–2979, 2003.
- Kawewong, A., Tangruamsub, S., and Hasegawa, O.: Position-invariant Robust Features for Long-term Recognition of Dynamic Outdoor Scenes, IEICE Trans. Information and Systems, E93-D, 9, 2587–2601, 2010.
- Kohonen, T.: Self-Organizing Maps, Springer Series in Information Sciences, 1995.
- Lowe, D. G.: Object Recognition from Local Scale-Invariant Features, Proc. IEEE I. Conf. Com. Vis., 2, 1150–1157, 1999.
- Luo, J., Pronobis, A., Caputo, B., and Jensfelt, P.: The KTHIDOL2 database, Technical Report CVAP304, Kungliga Tekniska Hoegskolan, CVAP/CAS, 2006.
- Madokoro, H., Utsumi, Y., and Sato, K.: Unsupervised Scene Classification Based on Context of Features for a Mobile Robot, Proc. 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Part I, 446–455, 2011.
- Madokoro, H. and Takahashi, J.: Generic Object Recognition Based on Unsupervised Learning Using Mobile Robot, Proc. Tateishi Science and Technology Fundation, 21, 87–90, 2012.
- Madokoro, H., Utsumi, Y., and Sato, K.: Scene Classification Using Unsupervised Neural Networks for Mobile Robot Vision, Proc. Society of Instrument and Control Engineers Annual Conference, 1568–1573, 2012.
- Maeyama, S., Ohya, A., and Yuta, S.: Long Distance Outdoor Navigation of an Autonomous Mobile Robot by Playback of Perceived Route Map, Proc. Fifth Int'l Symp. Experimental Robotics, 185–194, 1997.
- Matsumoto, Y., Inaba, M., and Inoue, H.: View-Based Approach to Robot Navigation, Proc. Int'l Conf. Intelligent Robots and Systems, 1702–1708, 2000.
- McQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations, Proc. Fifth Berkeley Symposium on Mathematical Statistics and Probability, 281–297, 1967.
- Morioka, H., Yi, S., Tongprasit, N., and Hasegawa, O.: Visual SLAM in Crowded Environments and Mobile Robot Navigation, Proc. the 28th Annual Conference of the Robotics Society of Japan, 2010.
- Nagahashi, T., Ihara, A., and Fujiyoshi, H.: Tendency of Image Local Features that are Effective for Discrimination by using Bag-of-Features in Object Category Recognition, IPSJ SIG Notes Computer Vision and Image Media, 3, 13–20, 2009.
- Nielsen, R. H.: Counterpropagation networks, Appl. Optics, 26, 4979–4983, 1987.
- Oliva, A. and Torralba, A.: Building the gist of a scene: the role of global image features in recognition, Visual Perception, Prog. Brain Res., 155, 23–26, 2006.
- Pronobis, A., Xing, L., and Caputo, B.: Overview of the CLEF 2009 Robot Vision Track, Proc. 10th international conference on Cross-language evaluation forum: multimedia experiments, 2010.
- Quattoni, A. and Torralba, A.: Recognizing Indoor Scenes, Proc. Computer Vision and Pattern Recognition, 413–420, 2009.
- Shi, J. and Malik, J.: Normalized Cut and image Segmentation, IEEE T. Pattern Anal., 22, 8881–8905, 2000.
- Siagian, C. and Itti, L.: Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attentinon, IEEE T. Pattern Anal., 29, 300–312, 2007.
- Takeuchi, T.: Underlying Mechanisms of Scene Recognition and Visual Search, ITE Technical Report, 33, 24, 7–14, 2009.
- Terashima, M., Shiratani, F., and Yamamoto, K.: Unsupervised Cluster Segmentation Method Using Data Density Histogram on Self-Organizing Feature Map, The transactions of the Institute of Electronics, Information and Communication Engineers, J79-D-II, 7, 1280–1290, 1996.
- Thrun, S.: Finding Landmarks for Mobile Robot Navigation, Proc. IEEE Int'l Conf. Robotics and Automation, 958–963, 1998.
- Torralba, A., Murphy, K. P., Freeman, W. T., and Rubin, M. A.: Context-Based Vision System for Place and Object Recognition, Proc. IEEE Int'l Conf. Computer Vision, 1023–1029, 2003.
- Torralba, A.: How many pixels make an image?, Visual Neurosci., 26, 123–131, 2009.
- 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.
- Ultsch, A.: Clustering with SOM U×C, Proc. Workshop on Self-Organizing Maps, 75–82, 2005.
- Utsumi, Y., Tsukada, M., Madokoro, H., and Sato, K.: Selection of SIFT Feature Points for Scene Description in Robot Vision, Proc. IEEE Sys. Man Cybern., 2276–2281, 2010.
- Wu, J., Christensen, H. I., and Rehg, J. M.: Visual Place Categorization: Problem, Dataset, and Algorithm, Proc. IEEE/RSJ Int'l Conf. Intelligent Robots and Systems, 4763–4770, 2009.
- Yanai, K.: The Current State and Future Directions on Generic Object Recognition, IPSJ SIG Notes Computer Vision and Image Media, 121–134, 2006.
- More Articles (30)
|
|