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Now showing items 1 - 3 of 3

  • A Novel Method for Broiler Abnormal Sound Detection Using WMFCC and HMM

    Liu, Longshen   Li, Bo   Zhao, Ruqian   Yao, Wen   Shen, Mingxia   Yang, Ji  

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  • An automatic ear base temperature extraction method for top view piglet thermal image

    Lu, Mingzhou   He, Ju   Chen, Chao   Okinda, Cedric   Shen, Mingxia   Liu, Longshen   Yao, Wen   Norton, Tomas   Berckmans, Daniel  

    Ear bases are considered the thermal windows of a piglet. Temperature variation in piglet ear bases can be used as the indicator of a piglet's health status. However, piglet skin temperatures in thermal windows in the existing research are obtained manually from infrared thermal images captured by a thermography. This has put an obstacle at the automatic identification of piglets with health disorder. An algorithm was proposed in this paper to extract ear base temperature automatically from top view piglet thermal images. Firstly, a SVM (Support Vector Machine) classifier was trained to identify piglet head part. Then, two ear base points were located based on the shape feature of the head part contour. Finally, two maximum temperatures inside the two circles centered by ear base points were extracted as the ear base temperatures. The proposed algorithm was implemented in Matlab (R) (R2016a) and applied to 100 testing images. The extracted ear base temperatures were compared with those extracted manually by using Fluke SmartView 3.14 (FLUKE Systems). Comparison results showed that for left and right ear base respectively, 97% and 98% of the testing images had an error within 0.4 degrees C. Ear base temperatures with such accuracy provided a foundation for the automatic identification of sick piglets.
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  • An automatic splitting method for the adhesive piglets' gray scale image based on the ellipse shape feature

    Lu, Mingzhou   Xiong, Yingjun   Li, Kunquan   Liu, Longshen   Yan, Li   Ding, Yongqian   Lin, Xiangze   Yang, Xiaojing   Shen, Mingxia  

    The average weight of piglets in lactation can be monitored automatically by piglets' average weight monitoring systems which are designed based on wireless multimedia sensor networks. Piglets counting in an automatic manner for piglets images is the foundation of these systems. Adhesive piglets may exist in an image due to the social character of piglets, which challenges the image splitting and automatic piglets counting. This paper proposes a segmentation algorithm for adhesive piglets images based on ellipse fitting method. Firstly, ellipse fitting is implemented for a large number of images which have one piglet. Parameters range of ellipses fitted by images with a single piglet on different age is extracted. Secondly, contours of connected components in an adhesive piglets image are extracted. Each contour is segmented based on concave points. Ellipse fitting is implemented for each contour segment. Finally, 5 rules for ellipse merging are put forwarded, which are used to merge anomalous ellipses. After ellipse merging, the number of ellipses equals the number of piglets. The proposed algorithm is applied to adhesive piglets images in Matlab R2012b and the experimental results show that the counting accuracy exceeds 86% when the number of piglets is less than 7. The algorithm provides the foundation for the piglets' average weight monitoring systems. (c) 2015 Elsevier B.V. All rights reserved.
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