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    188宝金博页面版: skin detection in luminance images using threshold technique skin detection in luminance im

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    内容提示： Skin Detection in Luminance Images using Threshold Technique International Journal of The Computer, the Internet and Management Vol. 15#1 (January – April, 2007) pp 25 -32 25Skin Detection in Luminance Images using Threshold Technique Hani. K. Almohair, Abd Rahman Ramli, Elsadig A. M., Shaiful J. Hashim Department of Computer and Communication Systems Engineering Faculty of Engineering University Putra Malaysia, 43400, Upm Serdang, Selangor, Malaysia Abstract With the development of the chromati...

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    Skin Detection in Luminance Images using Threshold Technique International Journal of The Computer, the Internet and Management Vol. 15#1 (January – April, 2007) pp 25 -32 25Skin Detection in Luminance Images using Threshold Technique Hani. K. Almohair, Abd Rahman Ramli, Elsadig A. M., Shaiful J. Hashim Department of Computer and Communication Systems Engineering Faculty of Engineering University Putra Malaysia, 43400, Upm Serdang, Selangor, Malaysia Abstract With the development of the chromatic skin color detection models, the threshold value is a useful and forceful element to discriminate between the skin and non-skin pixels. This paper reports an experimental investigation, which aims at using different threshold values to classify the human skin. The chromatic skin color model was used to model skin color in the r-g chromaticity space. This model based on testing database containing many of human images. It has been found that the threshold value is an important tool for increasing the human skin regions detected in color images containing luminance. Keywords: Skin chrominance models, Pixels classification, Skin detection. 1. Introduction It has been reached by researchers that skin color models can be used for detecting human skin in various computer vision applications. (Yang et al, 1998) suggested a real-time face tracker using an adaptive skin color model. They modeled the rg-chromaticities of skin as a 2D Gaussian distribution. (Jones compared a Bayesian approach to a mixture of Gaussian in RGB space. This model and Rehg, 1999) includes nearly 1 billion labeled training pixels obtained from Internet. (Jedynak et al, 2002) considered a sequence of three models for skin detection built from a large collection of labeled images. Each model is a maximum entropy model with respect to constraints concerning distributions. Several models were proposed for detecting human skin in various lighting conditions. (Soriano et al, 2000) presented a face tracking method that is adaptive to varying illumination color. (Storring and Andersen, 1999) proposed model for detecting human skin under changing lighting conditions. (Cho et al, 2001) presented a new skin color filter that is useful and capable of finding skin regions in images and adaptively adjust its threshold. While (Caetano et al, 2003) compared between single model and several Gaussian mixture models used to improve skin detection. This paper presents the impact of adjusting the threshold value in the chromatic skin color model for improving skin detection in images that contain luminance. 2. Chromatic Model The various color spaces can be easily converted to a RGB representation, A triple [r,g,b] doesn’t represent only color but also

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