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 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.

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.

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

2015 ◽  
Vol 743 ◽  
pp. 317-320
Author(s):  
Ravi Subban ◽  
Pasupathi Perumalsamy ◽  
G. Annalakshmi
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Skin Detection ◽  
Color Spaces ◽  
Color Component ◽  
Skin Segmentation ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Comprehensive Knowledge ◽  
Novel Method

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

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.

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 Deteksi Api pada Video dengan Gaussian Mixture Model Untuk Deteksi Gerakan Dan Segmentasi Warna Api Dalam Ruang Warna YCbCr

2021 ◽  
Vol 3 (1) ◽  
pp. 108-119
Author(s):  
Ristirianto Adi ◽  
I Gede Pasek Suta Wijaya
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Processing Speed ◽  
Color Space ◽  
Gaussian Mixture ◽  
Color Segmentation ◽  
Test Results ◽  
Ycbcr Color Space ◽  
Property Loss ◽  
Better Than

Fire is a disaster that can endanger lives and cause property loss. The solution to detect fire that is commonly used today is to use a sensor. Fire sensors can be used together with surveillance cameras (CCTV) which are now being installed in many office buildings. This study tries to build a model for detecting fire in video with a digital image processing approach using the Gaussian Mixture Model for motion detection and fire color segmentation in the YCbCr color space. The model is then tested with metrics for accuracy, precision, recall, and processing speed. The dataset used is in the form of videos with small, medium, large fire sizes, and videos that only have fire-like objects. The test results show that the algorithm is able to detect fire when the size of the fire is not too small or the position of the fire is close enough to the camera. For videos with a resolution of 800x600 and a framerate of 30 fps, it can achieve 66.89% accuracy, 73.77% precision, and 66.66% recall. The performance during the day is relatively better than at night. Algorithm processing speed is too slow to be implemented in real-time

scholarly journals Color Space to Detect Skin Image: The Procedure and Implication

2017 ◽  
Vol 4 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Helmie Arif Wibawa
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Image Data ◽  
Skin Detection ◽  
Test Results ◽  
Color Spaces ◽  
Color Feature

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

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