TY - GEN
T1 - Automatic segmentation of color lip images based on morphological filter
AU - Li, Meng
AU - CHEUNG, Yiu Ming
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - This paper addresses the problem of lip segmentation in color space, which is a crucial issue to the success of a lip-reading system. We present a new segmentation approach to lip contour extraction by taking account of the color difference between lip and skin in color space. Firstly, we obtain a lip segment sample via a color transformation sequence in 1976 CIELAB and LUX color spaces. Secondly, we establish a Gaussian model and make use of the hue and saturation value of each pixel within the lip segment to estimate the model parameters. Subsequently, the memberships of lip and non-lip regions are calculated, respectively. Thirdly, we employ morphological filters to obtain the desirable lip region approximately based on the memberships. Finally, we extract the lip contour via convex hull algorithm with the prior knowledge of the human mouth shape. Experiments show the efficacy of the proposed approach in comparison with the existing lip segmentation methods.
AB - This paper addresses the problem of lip segmentation in color space, which is a crucial issue to the success of a lip-reading system. We present a new segmentation approach to lip contour extraction by taking account of the color difference between lip and skin in color space. Firstly, we obtain a lip segment sample via a color transformation sequence in 1976 CIELAB and LUX color spaces. Secondly, we establish a Gaussian model and make use of the hue and saturation value of each pixel within the lip segment to estimate the model parameters. Subsequently, the memberships of lip and non-lip regions are calculated, respectively. Thirdly, we employ morphological filters to obtain the desirable lip region approximately based on the memberships. Finally, we extract the lip contour via convex hull algorithm with the prior knowledge of the human mouth shape. Experiments show the efficacy of the proposed approach in comparison with the existing lip segmentation methods.
UR - http://www.scopus.com/inward/record.url?scp=78049379464&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15819-3_51
DO - 10.1007/978-3-642-15819-3_51
M3 - Conference proceeding
AN - SCOPUS:78049379464
SN - 3642158188
SN - 9783642158186
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 384
EP - 387
BT - Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
T2 - 20th International Conference on Artificial Neural Networks, ICANN 2010
Y2 - 15 September 2010 through 18 September 2010
ER -