Title :
Cattle´s fur detection in complex background based on Graph Cuts
Author :
Fahmi, Hisyam ; Noviyanto, Ary ; Arymurthy, Aniati Murni
Author_Institution :
Image Process. & Pattern Recognition Lab., Univ. Indonesia, Depok, Indonesia
Abstract :
Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopted to detect cattle object in an image with complex background. The RGB colors and Gray Level Co-occurrence Matrix (GLCM) textures are used as the features set. This method can robustly segment the cattle beef image from its background. This segmentation method produces the average of accuracy value up to 90%.
Keywords :
Gaussian processes; feature extraction; graph theory; image colour analysis; image segmentation; image texture; matrix algebra; mixture models; GLCM texture; Gaussian mixture model; RGB colors; cattle beef image segmentation method; cattle fur detection; cattle object detection; complex background; feature set; graph cut method; gray level co-occurrence matrix texture; initialization label; overlapping characteristics; Accuracy; Cows; Equations; Gaussian mixture model; Image color analysis; Image segmentation; Mathematical model;
Conference_Titel :
Advanced Computer Science and Information Systems (ICACSIS), 2013 International Conference on
Conference_Location :
Bali
DOI :
10.1109/ICACSIS.2013.6761587
