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- W4309113324 abstract "Color as a feature has advantages like it is invariant to scaling, rotation, and partial occlusion changes. Skin color segmentation has many applications like sign language recognition, hand and face gesture recognition, biometric applications, face detection, and analysis of facial expressions. Due to the importance of an effective skin segmentation method, the Machine Learning (ML)-based skin segmentation approaches are studied in this paper. The objective of the work is to segment the different human skin tones for sign language recognition. The skin segmentation dataset from the UCI machine learning repository is used to evaluate the effect of various supervised learning algorithms. Despite the comparison criteria, KNN is found to be the desirable classifier. There are two color spaces RGB and HSV, considered in experiments, and the HSV representation gives better performance in the segmentation of various skin tones than the RGB color space." @default.
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- W4309113324 date "2022-11-16" @default.
- W4309113324 modified "2023-10-17" @default.
- W4309113324 title "Different Skin Tone Segmentation from an Image Using KNN for Sign Language Recognition" @default.
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- W4309113324 doi "https://doi.org/10.1007/978-981-19-4182-5_9" @default.
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