TY - GEN
T1 - Cattle's fur detection based on Gaussian mixture model in complex background
T2 - 2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
AU - Noviyanto, Ary
AU - Arymurthy, Aniati Murni
PY - 2012
Y1 - 2012
N2 - Segmentation becomes a difficult task if the objects and background are not homogeneous and having overlapping characteristics. Cattle segmentation from its background is required in several typical applications, such as: the automatic cattle race classification. The cattle's fur detection which is inspired from the human skin detection is investigated in this paper for cattle and background segmentation in automatic beef cattle race classification. The Gaussian mixture model that was used in skin detection has been adopted to model Bali cow and Hybrid Ongole cow in this beef cattle race classification. The RGB color space and two texture descriptors are used as the features set. The addition of texture descriptor has increased the performance of the fur detection and automatic race classification. The GMM performs well but the noise and the complexity of the background lead to misclassification.
AB - Segmentation becomes a difficult task if the objects and background are not homogeneous and having overlapping characteristics. Cattle segmentation from its background is required in several typical applications, such as: the automatic cattle race classification. The cattle's fur detection which is inspired from the human skin detection is investigated in this paper for cattle and background segmentation in automatic beef cattle race classification. The Gaussian mixture model that was used in skin detection has been adopted to model Bali cow and Hybrid Ongole cow in this beef cattle race classification. The RGB color space and two texture descriptors are used as the features set. The addition of texture descriptor has increased the performance of the fur detection and automatic race classification. The GMM performs well but the noise and the complexity of the background lead to misclassification.
UR - http://www.scopus.com/inward/record.url?scp=84875086320&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84875086320
SN - 9789791421157
T3 - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
SP - 185
EP - 189
BT - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
Y2 - 1 December 2012 through 2 December 2012
ER -