Gesture Recognition of Pigs Based on Wavelet Moment and Probabilistic Neural Network

Jin Jin Zhou; Wei Xing Zhu

doi:10.4028/www.scientific.net/AMM.687-691.3691

Paper Titles

Frog and Haze Image Enhancement Using Improved Histogram Equalization Method in HIS Color Space
p.3671

Nodes' Adjacency Relation of Video Sensor Network Based on GIS and GPS
p.3675

Alleviating Reactive Bit Jamming in Wireless Sensor Networks
p.3679

Application of Fuzzy Recognition Model for the Diversion Structures Safety Evaluation at Small Hydropower Stations
p.3686

Gesture Recognition of Pigs Based on Wavelet Moment and Probabilistic Neural Network
p.3691

Image Segmentation of Pig Using Wavelet Modulus and Edge Growth
p.3695

Optimization Algorithm Analysis on Satellite Communication Signal Processing
p.3699

Multi-View Radar Target Recognition Based on Bayesian Compressive Sensing
p.3704

The Selection of Transmitter Elevation in a Ka-Band HAP-Based Communication System under Fog Attenuation
p.3709

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Gesture Recognition of Pigs Based on Wavelet...

Gesture Recognition of Pigs Based on Wavelet Moment and Probabilistic Neural Network

Abstract:

For real-time monitoring the behavior of pigs in piggery, the method that combined the advantages of wavelet multi-scale analysis with invariant moments is proposed. Firstly, the original image is pre-processed by using ant colony algorithm to extract object contour. Then the target contour edge growth method and binary morphology are used, and the outlines of pigs are extracted by canny operator. Wavelet moment was used to get the global features of an image and increase the structural details of the image feature description. Finally, the neural network is applied to identify four behaviors including normal walking, walking down, looked up walking and lying of pigs. Experimental results show that the accuracy of the classification and identification of swine gesture reached more than 95%. This method has a better effect in the recognition of pigs and the noise resistance.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

3691-3694

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.3691

Citation:

Cite this paper

Online since:

November 2014

Authors:

Jin Jin Zhou*, Wei Xing Zhu

Keywords:

Ant Colony Algorithm (ACA), Neural Network, Wavelet Moment

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] T. Leroy,E. Vranken,E. Struelens,B. Sonck,D. Berckmans. Computer vision based on recogniti- on of behavior phenotypes of laying hens[C]. ASAE Annual International Meeting, (2005).

DOI: 10.13031/2013.19471

Google Scholar

[2] Cangara O, Leroy T, M Guarinob, et al. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis[J]. computers and electronics in agriculture. 2008, 64 : 53-60.

DOI: 10.1016/j.compag.2008.05.014

Google Scholar

[3] Liu yishu, Yang lihua, Sun qian. Journal of Image and Graphics. Vol 9, No. 3 Mar. (2004).

Google Scholar

[4] Chandan Singh, Ekta Walia, Rahul Upneja. Accuratecalculation of Zernike moments. Informa- tion Sciences 233 (2013) 255–275.

DOI: 10.1016/j.ins.2013.01.012

Google Scholar

[5] Sattar F, Salomonsson G . The use of a filter bank and the Wigner-Ville distribution for time-frequency representation [J]. IEEE Transactions on Signal Processing, 1999, 47(6).

DOI: 10.1109/78.765169

Google Scholar

[6] Marcano-Cedeno, Quintanilla-Dominguez, Cortina-Januchs. Feature Selection Using Seq- uential Forward Selection and classification applying Artificial Metaplasticity Neural Network[C]. 36th Annual Conference of the IEEE Industrial Electronics Society, IECON (2010).

DOI: 10.1109/iecon.2010.5675075

Google Scholar