Abstract
Computational algorithms of image processing were developed and evaluated to select, by motion detection, images of resting artificial pigs and to segment the pigs (mixture of black and white pigs) from their background. Motion detection of the pigs was implemented by detecting interframe differences of postural behavioral images. This algorithm combines the advantages of likelihood ratio method and shading model method and shows a stable performance under noisy and dynamic illumination conditions. Segmentation of the pigs from their background was implemented by employing multilevel thresholding and background reference techniques. The algorithm automatically determines the number of thresholds needed and produces satisfactory segmentation when both black and white pigs with different image intensities are present at the same time (the most complicated situation). The reference background image is updated so that temporal changes in illumination and/or spatial changes of the pen condition have little effect on the performance of image segmentation. The algorithm employs statistical models of the pigs and background and Bayes hypothesis testing to obtain and update the exposed portion of the reference background. Linear filters were used in this process for updating the parameters. These algorithms will serve as essential components for a novel, behavior-based, interactive approach to assess and control thermal comfort of group-housed pigs, which is expected to result in enhanced animal health and well-being.
Article PDF
Similar content being viewed by others
References
Boon, C. R. (1981). The effect of departure from lower critical temperature on group postural behavior of pigs.Animal Production,33, 71–79.
Geers, R., Goedseels, M., &Parduyus, G. (1986). Group postural behavior of growing pigs in relation to air velocity, air and floor temperature.Applied Animal Behaviour Science,16, 353–362.
Geers, R., Ville, H., Goedseels, V., Houkes, M., Goossens, K., Parduyns, G., &Van Bael, J. (1991). Environmental temperature control by the pigs’ comfort behavior through image analysis.Transactions of the ASAE,34, 2583–2586.
Hu, J. (1998).Development of image processing algorithms for automatic segmentation and selection of swine postural behaviors used in an interactive environmental control. Unpublished master’s thesis, Iowa State University.
Kapur, J. N., Sahoo, P. K., &Wong, A. K. C. (1985). A new method for gray level picture thresholding using the entropy of histogram.Computer Vision, Graphics, & Image Processing,29, 273–285.
Lee, S. U., Chung, S. Y., &Park, R. H. (1990). A comparative performance study of several global thresholding techniques for segmentation.Computer Vision, Graphics, & Image Processing,52, 171–190.
Levine, M. D., &Nazif, A. M. (1985). Dynamic measurement of computer generated image segmentation.IEEE Transactions on Pattern Analysis & Machine Intelligence,7, 155–164.
Otsu, N. (1979). A threshold selection method from gray level histograms.IEEE Transactions on Systems, Man & Cybernetics,9, 62–66.
Pal, N. R., &Pal, S. K. (1993). Review on image segmentation techniques.Pattern Recognition,26, 1277–1294.
Sahoo, P. K., Soltani, S., Wong, A. K. C., &Chen, Y. C. (1988). A survey of thresholding techniques.Computer Vision, Graphics, & Image Processing,41, 233–260.
Shao, J. (1997).Classification of swine thermal comfort behavior by image processing and neural network. Unpublished doctoral dissertation, Iowa State University.
Shao, J., Xin, H., &Harmon, J. D. (1997). Neural network analysis of postural behavior of young swine to determine their thermal comfort state.Transactions of the ASAE,40, 755–760.
Shao, J., Xin, H., &Harmon, J. D. (1998). Comparison of image feature extraction for classification of swine thermal comfort behavior.Computer & Electronics in Agriculture,19, 223–232.
Skifstad, K., &Jain, R. (1989). Illumination independent change detection for real world image sequences.Computer Vision, Graphics, & Image Processing,46, 387–399.
Wouters, P., Geers, R., Parduyns, G., Goossens, K., Truyen, B., Goedseels, V., &Van deer Stuyft, E. (1990). Image analysis parameters as inputs for automatic environmental temperature control in the piglets’ houses.Computers & Electronics in Agriculture,5, 233–246.
Xin, H., &Shao, J. (1997). Application of machine vision to swine environmental control.Proceedings of Advanced Intelligent Mechatronics 1997. Tokyo: Waseda University.
Yakimovsky, Y. (1976). Boundary and object detection in real world images.Journal of the Association for Computing Machinery,23, 599–618.
Yin, P. Y., &Chen, L. H. (1997). A fast iterative scheme for multilevel thresholding methods.Signal Processing,60, 305–313.
Author information
Authors and Affiliations
Corresponding author
Additional information
Journal Paper No J-18154 of the Iowa Agriculture and Home Economics Experiment Station, Iowa State University, Project No. 3355. Funding for this study was provided in part by the Special Research Initiation Grant of Iowa State University and the Iowa Pork Producers Association.
Rights and permissions
About this article
Cite this article
Hu, J., Xin, H. Image-processing algorithms for behavior analysis of group-housed pigs. Behavior Research Methods, Instruments, & Computers 32, 72–85 (2000). https://doi.org/10.3758/BF03200790
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/BF03200790