Boundary detection of pigs in pens based on adaptive thresholding using an integral image and adaptive partitioning
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Date
2017-04
Authors
Buayai, Prawit
Kantanukul, Tatpong
Leung, Carson K.
Runapongsa Saikaew, Kanda
Journal Title
Journal ISSN
Volume Title
Publisher
CMU Press
Abstract
Boundary detection of pigs is important to pig weight estimation, pig feeding behavior analysis, and thermal comfort control. This research proposes a boundary detection method for pigs in a feeder zone with a high-density pen under insufficient and varied lighting, a dirty pen scene, and small field of view. The method is based on adaptive thresholding using an integral image and adaptive partitioning. First, we segment an original grayscale image with adaptive thresholding using an integral image, and then apply adaptive partitioning with connected components. Afterwards, we utilize the maximum entropy threshold of each partition and merge the results. Our experimental results using 230 images showed that the proposed method led to a high average detection rate in a short execution time. Moreover, to the best of our knowledge, our study is the first attempt to investigate pig boundary detection in a practical farm environment, which involved dirty pen scenes with insufficient and varied lighting.
Description
P. Buayai, T. Kantanukul, C.K. Leung, and K. Runapongsa Saikaew. 2017. Boundary detection of pigs in pens based on adaptive thresholding using an integral image and adaptive partitioning. CMU Journal of Natural Sciences, 16(2): 145-155 (April-June 2017). This paper is published in CMU Journal of Natural Sciences, which is peer-reviewed and published as hardcopy and online open-access journal.
Keywords
natural sciences, computer science, pig boundary detection, image segmentation, adaptive partitioning, adaptive thresholding
Citation
P. Buayai, T. Kantanukul, C.K. Leung, and K. Runapongsa Saikaew. 2017. Boundary detection of pigs in pens based on adaptive thresholding using an integral image and adaptive partitioning. CMU Journal of Natural Sciences, 16(2): 145-155 (April-June 2017)