Artículos de revistas
HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION
Fecha
2008Registro en:
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.22, n.6, p.1241-1265, 2008
0218-0014
10.1142/S0218001408006739
Autor
BONVENTI JR., Waldemar
COSTA, Anna Helena Reali
Institución
Resumen
In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.