The Research on Hand Gestures Segmentation Based on Skin Color Detection Combination with Dynamic Threshold

2013 ◽  
Vol 756-759 ◽  
pp. 1938-1942 ◽  
Author(s):  
Yi Ting Wang ◽  
Feng Jing Shao
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Color Segmentation ◽  
Dynamic Threshold ◽  
Background Color ◽  
Threshold Method ◽  
Hand Gestures ◽  
Skin Color Detection ◽  
Color Detection ◽  
Rgb Color Space

Aiming at the shortcomings of hand gestures segmentation based on fixed threshold and the problem of the interference of background color, this paper proposes a research on hand gestures segmentation based on skin color detection. The captured image is translated to YCgCr color space from RGB color space, and then skin color segmentation is done by using dynamic threshold method, so the hand gestures segmentation is completed. Finally segmentation results are verified by experiments and the method is summarized.

scholarly journals Skin Color Segmentation in RGB Color Space by Adaptive Network Based Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 37 ◽  
pp. 264 ◽  
Author(s):  
Mohammad Hussein Fakhravari ◽  
Marzieh Dadvar
Keyword(s):  
Fuzzy Inference System ◽  
Skin Color ◽  
Fuzzy Inference ◽  
Color Space ◽  
Color Segmentation ◽  
Data Mapping ◽  
Adaptive Network ◽  
Inference System ◽  
Wide Range ◽  
Rgb Color Space

Skin color detection is a popular and useful technique because of the wide range of application in both human computer interactions and analyses based on diagnostic. Therefore, providing an appropriate method for pixel-like skin parts can solve many problems. The presented color segmentation algorithm works directly in RGB color space without having to convert the color space. Using Genfis3 function, we formed the Sugeno fuzzy network and clustered the data using fuzzy C-Mean (FCM) clustering rule and for each class and cluster we considered a Rule. In the next step, the output resulting from pseudo-polynomial data mapping is provided as the input to Adaptive Network Based Fuzzy Inference System (ANFIS).

An Improved Skin Color Detection Algorithm Model

2013 ◽  
Vol 756-759 ◽  
pp. 3517-3521
Author(s):  
Xue Feng Dong
Keyword(s):  
Skin Color ◽  
System Analysis ◽  
Detection System ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Model ◽  
Skin Color Detection ◽  
Color Detection ◽  
Skin Color Model ◽  
Color Mask

The main objective of designing skin color model is to determine whether the pixel is skin color pixels and generate the skin color mask images. The paper discusses the choise of color space and skin color model designing in skin color detection system, analysis the problems often needed to solve in it and put forward an improved skin color detection algorithm model based on ellipse boundary. The skin color detection experiment is completed. The result of experiment shows the skin color detection algorithm model is good.

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 Method of Color Detection Base on Xy-H

2012 ◽  
Vol 263-266 ◽  
pp. 525-528
Author(s):  
Hua Yun Yu ◽  
Meng Yun Liu
Keyword(s):  
Comparative Analysis ◽  
Skin Color ◽  
Color Space ◽  
Distribution Area ◽  
Two Dimensional ◽  
Threshold Segmentation ◽  
One Dimensional ◽  
Skin Color Detection ◽  
Color Detection

In order to improve the accuracy and speed of the color detection, to comparative analysis on skin color detection in the clustering of nine color space and regularity of distribution area, and some meaningful conclusions are discovered that the XYZ color space is suitable to do skin color two-dimensional threshold segmentation while the HSV、HIS、HSL is suitable to do one-dimensional threshold segmentation on tonal H component. A new color space xy-H is constructed for color detection using those conclusions which were discovered. The results show that it has significantly improved for color diction.

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