A Bayesian approach to skin color classification in YCbCr color space

2002 ◽  
Author(s):  
D. Chai ◽  
A. Bouzerdoum
Keyword(s):  
Bayesian Approach ◽  
Skin Color ◽  
Color Space ◽  
Color Classification ◽  
Ycbcr Color Space

A Skin Detection Algorithm Based on Bayes Decision in the YCbCr Color Space

2011 ◽  
Vol 121-126 ◽  
pp. 672-676 ◽  
Author(s):  
Xin Yan Cao ◽  
Hong Fei Liu
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Bayesian Decision ◽  
Color Classification ◽  
Color Distribution ◽  
Ycbcr Color Space ◽  
The Face ◽  
Good Coverage

Skin color detection is a hot research of computer vision, pattern identification and human-computer interaction. Skin region is one of the most important features to detect the face and hand pictures. For detecting the skin images effectively, a skin color classification technique that employs Bayesian decision with color statistics data has been presented. In this paper, we have provided the description, comparison and evaluation results of popular methods for skin modeling and detection. A Bayesian approach to skin color classification was presented. The statistics of skin color distribution were obtained in YCbCr color space. Using the Bayes decision rule for minimum cot, the amount of false detection and false dismissal could be controlled by adjusting the threshold value. The results showed that this approach could effectively identify skin color pixels and provide good coverage of all human races, and this technique is capable of segmenting the hands and face quite effectively. The algorithm allows the flexibility of incorporating additional techniques to enhance the results.

Human Body Parts Detection Using YCbCr Color Space

2013 ◽  
Vol 393 ◽  
pp. 556-560
Author(s):  
Nurul Fatiha Johan ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Human Body ◽  
Skin Color ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Color Model ◽  
Body Parts ◽  
Color Spaces ◽  
Morphological Operation ◽  
Ycbcr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

An Improved Otsu’s Thresholding Algorithm on Gesture Segmentation

2017 ◽  
Vol 21 (2) ◽  
pp. 247-250 ◽  
Author(s):  
Chongshan Lv ◽  
◽  
Ting Zhang ◽  
Chengyuan Liu
Keyword(s):  
Gesture Recognition ◽  
Skin Color ◽  
Color Space ◽  
Gaussian Model ◽  
Adaptive Threshold ◽  
Ycbcr Color Space ◽  
Gesture Segmentation ◽  
Rgb Color Space ◽  
Similarity Graph ◽  
Thresholding Algorithm

In gesture recognition systems, segmenting gestures from complex background is the hardest and the most critical part. Gesture segmentation is the prerequisite of following image processing, and the result of segmentation has a direct influence on the result of gesture recognition. This paper proposed an algorithm of adaptive threshold gesture segmentation based on skin color. First of all, the image should be transformed from RGB color space to YCbCr color space. After eliminating luminance component Y, similarity graph of skin color will be obtained from the Gaussian model. Then Otsu adaptive threshold algorithm is used to carry out binary processing for the similarity graph of skin color. After the segmentation of skin color regions, the morphology method is used to process binary image for determining the location of hands. Experimental results show that the detailed segmentation of skin color using the dynamic-adaptive threshold can improve noise resistance and can produce better results.

scholarly journals Image Segmentation for Skin Detection

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Sajaa G. Mohammed ◽  
Abdulrahman H. Majeed ◽  
Ali Aldujaili ◽  
Enas Kh. Hassan ◽  
Safa S. Abdul-Jabbar
Keyword(s):  
Image Segmentation ◽  
Skin Color ◽  
Detection System ◽  
Color Space ◽  
Research Direction ◽  
Parametric Models ◽  
Future Research ◽  
Skin Detection ◽  
Ycbcr Color Space ◽  
Human Faces

Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measures the distances of pixel colors to skin tones. Results showed that the YCbCr color space performed better skin pixel detection than regular Red Green Blue images due to its isolation of the overall energy of an image in the luminance band. The RGB color space poorly classified images with wooden backgrounds or objects. Then, a histogram-based image segmentation scheme utilized to distinguish between the skin and non-skin pixels. The need for a compact skin model representation should stimulate the development of parametric models of skin detection, which is a future research direction.

Application of Skin Detection Based on Irregular Polygon Area Boundary Constraint on YCbCr and Reverse Gamma Correction

2011 ◽  
Vol 327 ◽  
pp. 31-36
Author(s):  
Bao Song Wang ◽  
Xue Qiang Lv ◽  
Xin Long Ma ◽  
Hong Wei Wang
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Real Life ◽  
Skin Detection ◽  
Gamma Correction ◽  
Ycbcr Color Space ◽  
Boundary Constraint ◽  
Polygon Area ◽  
Space Experiments ◽  
Better Than

YCbCr color space is widely used in skin detection. An improved method is brought up in this paper: a method based on irregular polygon area boundary constraint on YCbCr color space. Experiments shows that this method get more accurate distribution of skin color in YCbCr color space and lower the false detection rate while keeps the precision rate. In consideration of that the value of pix on image is not the real pix in real life, an improved reverse Gamma correction is brought up for solve a problem in reverse Gamma correction. Experiment result shows that improved reverse Gamma correction is better than none improved reverse Gamma correction.

scholarly journals Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space

2015 ◽  
Vol 57 ◽  
pp. 41-48 ◽  
Author(s):  
Khamar Basha Shaik ◽  
P. Ganesan ◽  
V. Kalist ◽  
B.S. Sathish ◽  
J. Merlin Mary Jenitha
Keyword(s):  
Comparative Study ◽  
Skin Color ◽  
Color Space ◽  
Ycbcr Color Space ◽  
Skin Color Detection ◽  
Color Detection

A Novel Approach of Face Detection Based on Double Skin Models and AdaBoost Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 740-743
Author(s):  
Shun Yan Hou ◽  
Jian Min Qie ◽  
Jing Xu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Space ◽  
Morphological Processing ◽  
Skin Segmentation ◽  
Adaboost Algorithm ◽  
Ycbcr Color Space ◽  
Novel Approach ◽  
Detection Approach ◽  
Double Skin

A novel face detection approach based on double skin models and AdaBoost algorithm is proposed in this paper. The image segemention of skin regions is firstly got with a fixed threshold skin model in YCbCr color space. The image segemention result is used for optimizing the parameters of Gaussian skin color model which is used for the image segmentation of the skin regions secondly. The logical operations are computed with the twice results of skin segmentation and then the coarse position of candidate face regions are got by morphological processing. Finally, the accurate face regions are acquired combined with Adaboost algorithm. The experimental results indicate that this face detection method can restrain the false number better on the premise that a proper detection rate should be kept and has better robustness.

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