Image-processing algorithms for behavior analysis of group-housed pigs.

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.