Journal
BIOSYSTEMS ENGINEERING
Volume 135, Issue -, Pages 54-60Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2015.05.001
Keywords
Group-housed pigs; Object extraction; Adaptive partitioning; Multilevel thresholding; Maximum Shannon entropy
Funding
- National Natural Science Foundation of China [31172243]
- Doctoral Program of the Ministry of Education of China [2010322711007]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Graduate Student Scientific Research Innovation Projects of Jiangsu Ordinary University [CXLX13_664]
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The aim of this study is to provide a feasible method that can accurately extract individual pigs from a drinker and feeder zone; therefore, an object extraction method based on adaptive partitioning and multilevel thresholding segmentation is proposed. First, a single frame image is enhanced using histogram equalisation, and then it is segmented with a maximum entropy global threshold. The initial segmentation objects are obtained by extracting a valid area and morphological processing. Then, each object centroid is calculated from the initially segmented objects, and the original image is adaptively divided into multiple circular sub-blocks whose origin is the centroid and radius is the maximum distance from the centroid to the edge point. Finally, an accurate secondary segmentation result is obtained using multilevel thresholding segmentation in each sub-block. The test data included thirty random videos collected in AVI format, and 9000 frames from 5 days x 6 videos x 120 s x 25 frames s(-1) were selected. Results show that the average detection rate is 92.5%. This paper also analyses the possible applications of the proposed method to pig behaviour analysis, individual recognition, and weight estimation. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.
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