Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
A New Method of Color Image Segmentation Based on Intensity and Hue Clustering
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
Adaptive learning of an accurate skin-color model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A segmentation algorithm for noisy images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Skin segmentation based face tracking independent of lighting conditions
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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In this paper, we present a novel algorithm to detect homogeneous color regions in images. We show its performance by applying it to skin detection. In contrast to previously presented methods, we use only a rough skin direction vector instead of a static skin model as a priori knowledge. Thus, higher robustness is achieved in images captured under unconstrained conditions. We formulate the segmentation as a clustering problem in color space. A homogeneous color region in image space is modeled using a 3D gaussian distribution. Parameters of the gaussians are estimated using the EM algorithm with spatial constraints. We transform the image by a whitening transform and then apply a fuzzy k-means algorithm to the hue value in order to obtain initialization parameters for the EM algorithm. A divisive hierarchical approach is used to determine the number of clusters. The stopping criterion for further subdivision is based on the edge image. For evaluation, the proposed method is applied to skin segmentation and compared with a well known method.