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Research on Beef Skeletal Maturity Determination Based on Shape Description and Neural Network

Journal from gdlhub / 2016-11-17 04:37:52
By : Xiangyan Meng, Yumiao Ren, Haixian Pan, Telkomnika
Created : 2015-06-01, with 1 files

Keyword : Beef Skeletal Maturity, Image Processing, Invariant Moments, Neural Network
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1468

Physiological maturity is an important indicator for beef quality. In traditional method, the maturity grade is determined by subjectively evaluating the degree of cartilage ossification at the tips of the dorsal spine of the thoracic vertebrae. This paper uses the computer vision to replace the artificial method for extracting object (cartilage and bone) regions. Hu invariant moments of object region were calculated as the regional shape characteristic parameters. A trained Hopfield neural network model was used for recognizing cartilage and bone area in thoracic vertebrae image based on minimum Euclidean distance. The result showed that the accuracy of network recognition for cartilage and bone region was 92.75% and 87.68%, respectively. For automatically maturity prediction, the accuracy of prediction was 86%. Algorithm proposed in this paper proved the image description and neural network modeling was an effective method for extracting image feature regions.

Description Alternative :

Physiological maturity is an important indicator for beef quality. In traditional method, the maturity grade is determined by subjectively evaluating the degree of cartilage ossification at the tips of the dorsal spine of the thoracic vertebrae. This paper uses the computer vision to replace the artificial method for extracting object (cartilage and bone) regions. Hu invariant moments of object region were calculated as the regional shape characteristic parameters. A trained Hopfield neural network model was used for recognizing cartilage and bone area in thoracic vertebrae image based on minimum Euclidean distance. The result showed that the accuracy of network recognition for cartilage and bone region was 92.75% and 87.68%, respectively. For automatically maturity prediction, the accuracy of prediction was 86%. Algorithm proposed in this paper proved the image description and neural network modeling was an effective method for extracting image feature regions.

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