Abstract
Several applications demand the segmentation of images in skin and non-skin regions, such as face recognition, hand gesture detection, nudity recognition, among others. Human skin detection is still a challenging task and, although color attribute is a very important clue, it usually generates high rate of false positives. This work proposes and analyzes a skin segmentation method improved by texture energy. Experimental results on a challenging public data set demonstrate significant improvement of the proposed skin segmentation method over color-based state-of-the-art approaches.
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Santos, A., Pedrini, H. (2015). Human Skin Segmentation Improved by Texture Energy Under Superpixels. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_5
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DOI: https://doi.org/10.1007/978-3-319-25751-8_5
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