Cattle's fur detection based on Gaussian mixture model in complex background: Application of automatic race classification of beef cattle

Ary Noviyanto, Aniati Murni Arymurthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
Pages185-189
Number of pages5
Publication statusPublished - 1 Dec 2012
Event2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Depok, Indonesia
Duration: 1 Dec 20122 Dec 2012

Publication series

Name2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings

Conference

Conference2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
CountryIndonesia
CityDepok
Period1/12/122/12/12

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