Abstract
A visual sensor network is one of the streams of sensor network in which an image from the sensor node is transmitted to the server to be processed further without human intervention. Object Recognition in specific applications such as agriculture, defense etc, requires only the object of importance to be captured and transmitted to the processing center. This can be done by training the visual sensor node using the multilayer feed-forward technique. In the proposed work, a fruit object recognition system has been developed using the multilayer feed-forward technique of the neural network by extracting features from the sample fruit images. The experimental results reveal average recognition rate of 94.23%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chen, F.: Simulation of wireless sensor nodes using SMAC, Master’s thesis, Department of Computer Science, University of Erlangen-Neuremberg (September 2005), http://dcg.ethz.ch/theses/ss05/mics-embedding-report.pdf
Seng, W.C., Mirisaee, S.H.: A New Method for Fruits Recognition System. In: 2009 International Conference on Electrical Engineering and Informatics, Selangor, Malaysia, August 5-7 (2009)
Shah Rizam, M.S.B., Farah Yasmin, A.R., Ahmad Ihsan, M.Y., Shazana, K.: Non-destructive Watermelon Ripeness Determination Using Image Processing and Artificial Neural Network (ANN). International Journal of Electrical and Computer Engineering 4(6) (2009)
Patel, H.N., Jain, R.K., Joshi, M.V.: Fruit Detection using Improved Multiple Features based Algorithm. International Journal of Computer Applications (0975 – 8887) 13(2) (January 2011)
Zhou, H.-Y., Luo, D.-Y., Gao, Y., Zuo, D.-C.: Modeling of Node Energy Consumption for Wireless Sensor Networks. Journal of Scientific Research, Wireless Sensor Network 3, 18–23 (2011)
Song, W.-G., Guo, H.-X., Wang, Y.: A Method of Fruits Recognition Based on SIFT Characteristics Matching. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence (2009)
Jiménez, A.R., Jain, A.K., Ceres, R., Pons, J.L.: Automatic fruit recognition: A survey and new results using Range/Attenuation images. Pattern Recognition 32(10), 1719–1736 (1999)
Yang, L., Dickinson, J., Wu, Q.M.J., Lang, S.: A Fruit Recognition Method for Automatic Harvesting. IEEE (2007)
Basu, J.K., Bhattacharyya, D., Kim, T.-H.: Use of Artificial Neural Network in Pattern Recognition. International Journal of Software Engineering and Its Applications 4(2) (April 2010)
Zilan, R., Barceló-Ordinas, J.M., Tavli, B.: Image Recognition Traffic Patterns for Wireless Multimedia Sensor Networks. EuroNGI Network of Excellence and CICYT TEC2004-06437-C05-05
Halgamuge, M.N., Zukerman, M., Ramamohanarao, K., Vu, H.L.: An estimation of sensor energy consumption. Progress In Electromagnetics Research B 12, 259–295 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kenchannavar, H.H., Domanal, S.G., Kulkarni, U.P. (2013). Context-Aware Information Processing in Visual Sensor Network. In: Das, V.V., Chaba, Y. (eds) Mobile Communication and Power Engineering. AIM 2012. Communications in Computer and Information Science, vol 296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35864-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-35864-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35863-0
Online ISBN: 978-3-642-35864-7
eBook Packages: Computer ScienceComputer Science (R0)