Building a Gaussian Model for Skin Color Distribution in YCbCr Color Space
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Resource Overview
Constructing a Gaussian model for skin color distribution in YCbCr color space to obtain skin probability likelihood images, followed by skin region segmentation using an optimal dynamic threshold selection algorithm.
Detailed Documentation
In the YCbCr color space, we can establish a Gaussian model for skin color distribution. This model enables us to generate a skin probability likelihood image through statistical modeling of chrominance components (Cb and Cr). The implementation typically involves calculating mean vectors and covariance matrices from training samples of skin pixels, followed by applying Gaussian probability density functions to estimate skin likelihood for each pixel. Subsequently, guided by an optimal dynamic threshold selection algorithm such as Otsu's method or adaptive thresholding, we can achieve accurate segmentation of skin regions. This approach enhances the identification and separation of skin areas, making it valuable for various applications in image processing and computer vision systems, including face detection, gesture recognition, and biometric analysis.
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