Abstract
This chapter presents a novel cellular neural network architecture for image binarization in video sequence. The cellular network is part of a neuro-inspired system used to detect dynamic objects in video sequences. Among its novelty is that besides binarization it is able to reduce also noise, and its parameters are self-adapted. Qualitative findings are used to show the advantage of using the cellular network.
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References
Yu, B., Zhang, L.: Pulse-coupled neural networks for contour and motion matching. IEEE Trans. Neural Netw. 15(5), 1186–1201 (2004)
Faro, A., Giordano, D., Spampinato, C.: Evaluation of the traffic parameters in a metropolitan area by fusing visual perceptions and CNN processing of webcam images. IEEE Trans. Neural Netw. 19(6), 1108–1129 (2008)
Costantini, G., Casali, D., Carota, M., Perfetti, R.: A CNN-based algorithm for moving object detection in stereovision applications. In: Proceedings of International Conference on Circuit Theory and Design European, pp. 500–503, Aug 2007
Wei-Song, L., An-Te, L., Chun-Hsiung, F.: Computational autonomous visual perception using cellular neural networks. In: Proceedings of IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 198–202, July 2005
Dubravko, C., Marques, O., Socek, D.: Neural network approach to background modeling for video object segmentation. IEEE Trans. Neural Netw. 18(6), 1614–1627 (2007)
Maddalena, L., Petrosino, A.: A self-organization approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)
Chacon-Murguia, M.I., Gonzalez-Duarte, S., Vega-Pineda, J.: Simplified SOM-neural model for video segmentation of moving objects. In: Proceedings of International Joint Conference on Neural Networks, pp. 14–19, June 2009
Chacon-Murguia, M.I., Gonzalez-Duarte, S.: An adaptive neural-fuzzy approach for object detection in dynamic backgrounds for surveillance systems. IEEE Trans. Ind. Electron. 59(8), 3286–3298 (2012)
Ghasemi, A., Safabakhsh, R.: Unsupervised foreground-background segmentation using growing self organizing map in noisy backgrounds. In: Proceedings of 3rd International Conference on Computer Research and Development, pp. 334–338, Mar 2011
Baier, V.: Motion perception with recurrent self-organizing maps based models. In: Proceedings of International Joint Conference on Neural Networks, pp. 1182–1186, July 2005
Avila-Mora, I.M., Castellanos-Sanchez, C.: Bio-inspired clustering of moving objects. In: Proceedings of Congress on Intelligent Systems, pp. 58–62, May 2009
Miikkulainen, R., Bednar, J.A., Choe, Y., Sirosh, J.: Computational Maps in the Visual Cortex. Springer Sciences Media Inc., Ed., New York (2005)
Brutzer, S., Höferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: Proceedings of Conference on Computer Vision and Pattern recognition, pp. 1937–1944, June 2011
Chua, L., Roska, T.: Cellular Neural Networks and Visual Computing Foundation and Applications. Cambridge University Press, New York (2004)
Acknowledgments
The authors thank the Fondo Mixto de Fomento a la Investigación Cientifica y Tecnologica CONACYT-Gobierno del Estado de Chihuahua under grant CHIH-2012-C03-193760 and Tecnologico Nacional de Mexico under grants CHI-MCIET-2013-230 and CHI-IET-2012-105.
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Chacon-Murguia, M.I., Ramirez-Quintana, J.A. (2015). Cellular Neural Network Scheme for Image Binarization in Video Sequence Analysis. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_8
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DOI: https://doi.org/10.1007/978-3-319-17747-2_8
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