Abstract
This paper introduces a novel framework for collaborative object recognition, which expands the applicability and improves the accuracy of object recognition. In this framework, a system not only recognizes targets but also detects and evaluates conditions that may make recognition difficult, and tries to resolve the situation by presenting the user with information on how to alter the conditions. The user can see how to make improvements, leading to correct recognition with little effort. The system can provide a useful, easy-to-use tool. In this research, a prototype system for kitchen scenes is designed, which can achieve situation evaluation and human-computer collaboration to improve recognition. We verified the framework by observing improvements in recognition accuracy and behavior of users in our experiments.
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References
Nigam, K., McCallum, A.K., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39, 103–134 (2000)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Ozeki, M., Miyata, Y., Aoyama, H., Nakamura, Y.: Collaborative Object Recognition through Interactions with an Artificial Agent. In: International Workshop on Human-Centered Multimedia, pp. 95–101 (2007)
Suh, B., Bedersona, B.B.: Semi-automatic photo annotation strategies using event based clustering and clothing based person recognition. Interacting with Computers 19(4), 524–544 (2007)
Takemura, N., Miura, J.: View Planning of Multiple Active Cameras for Wide Area Surveillance. In: IEEE International Conference on Robotics and Automation, pp. 3173–3179 (2007)
Shibata, M., Yasuda, Y., Ito, M.: Moving Object Detection for Active Camera based on Optical Flow Distortion. In: 17th World Congress 2008, International Federation of Automatic Control, pp. 14720–14725 (2008)
Guttmann, M., Wolf, L., Cohen-Or, D.: Semi-Automatic Stereo Extraction From Video. In: International Conference on Computer Vision, pp. 417–424 (2009)
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© 2010 Springer-Verlag Berlin Heidelberg
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Kondo, K., Nishitani, H., Nakamura, Y. (2010). Human-Computer Collaborative Object Recognition for Intelligent Support. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_44
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DOI: https://doi.org/10.1007/978-3-642-15696-0_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15695-3
Online ISBN: 978-3-642-15696-0
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