@article{fef0f1db70664c6884e8220191023424,
title = "Object segmentation using maximum neural networks for the gesture recognition system",
abstract = "In this paper, we present a new clustering method for segmentations of moving target and non-target objects. We assume that the moving target object has the following conditions: (1) object motion data continuity inter-frame, and (2) object motion data continuity intra-frame. In our model, clusters tend to form as filling these two conditions. The experimental results showed the effectiveness of the proposed algorithm and the performance of this model in terms of the quality of the recognition results. Our algorithm is able to clean the input noise by removing non-target objects before the recognition process.",
keywords = "Gesture recognition, Maximum neural networks, Object segmentation",
author = "Noriko Yoshiike and Yoshiyasu Takefuji",
note = "Funding Information: The authors thank the reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper. Noriko Yoshiike received her M.Sc. degree in media and government in 2000 from the Department of Media and Governance, Keio University. Since 2000 she has been working towards her Ph.D. in the same university. Her current research interests focus on neural network models and their applications. Yoshiyasu Takefuji is a tenured professor on faculty of environmental information at Keio University since April 1992 and was on tenured faculty of Electrical Engineering at Case Western Reserve University since 1988. Before joining Case, he taught at the University of South Florida for 2 years and the University of South Carolina for 3 years. He received his BS (1978), MS (1980), and Ph.D. (1983) in Electrical Engineering from Keio University under the supervision of Professor Hideo Aiso. His research interests focus on neural computing and hyperspectral computing. He received the National Science Foundation/Research Initiation Award in 1989 and received the distinct service award from IEEE Trans. on Neural Networks in 1992. ",
year = "2003",
month = apr,
doi = "10.1016/S0925-2312(02)00617-3",
language = "English",
volume = "51",
pages = "213--224",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",
}