scholarly journals An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Luo ◽  
Gaoxiang Cui ◽  
Deguang Li
Keyword(s):  
Gesture Recognition ◽  
Skin Color ◽  
Color Space ◽  
Segmentation Method ◽  
Data Set ◽  
Ycbcr Color Space ◽  
Open Operation ◽  
Gesture Segmentation ◽  
Recognition Success ◽  
Interactive Experience

With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.

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.

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 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.

Design of a Cognitive Rehabilitation System Based on Gesture Recognition

2020 ◽  
Author(s):  
Zhiqiang Teng ◽  
Haodong Chen ◽  
Qitao Hou ◽  
Wanbing Song ◽  
Chenchen Gu ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Cognitive Training ◽  
Gesture Recognition ◽  
Cognitive Rehabilitation ◽  
Color Space ◽  
Recognition Algorithm ◽  
Training System ◽  
Computer Assisted ◽  
Ycbcr Color Space ◽  
Computer Interaction

Abstract Computer-assisted cognitive training is an effective intervention for patients with mild cognitive impairment (MCI), which can avoid the disadvantages of traditional cognitive training that consumes a lot of medical resources and is difficult to be standardized. However, many computer-assisted cognitive training systems have unfriendly human-computer interaction, for not considering that most MCI patients have certain difficulties in using computers. In this paper, we design a cognitive training system which allows patients to implement human-computer interaction through gestures. First, a gesture recognition algorithm is proposed, in which we implement gesture segmentation based on YCbCr color space and Otsu algorithm, extract Fourier Descriptors of gesture contour as feature vectors and use SVM algorithm to train a classifier to recognize gestures. Then, the graphical user interface (GUI) of the system is designed to realize the task requirement of cognitive training for the MCI patients. Finally, the results of tests show the accuracy of the algorithm and the feasibility of the GUI. With the above computer-assisted cognitive training system, patients can achieve human-computer interaction only through gestures without the need to use keyboard, mouse, etc., greatly reducing the burden of patients during training.

scholarly journals Robust Digital Image Watermarking in YCbCr Color Space using Hybrid Method: A Survey

2021 ◽  
Vol 9 (1) ◽  
pp. 1195-1199
Author(s):  
Tushar Rohilla, Manoj Kumar, Rajeev Kumar
Keyword(s):  
Image Watermarking ◽  
Research Work ◽  
Color Space ◽  
Primary Phase ◽  
Second Phase ◽  
Final Phase ◽  
Security Issue ◽  
Data Set ◽  
Ycbcr Color Space ◽  
Video Security

This paper provides the conceptual framework on image watermarking which is widely used for security purpose within the epoch of data and Communication Technology. Image watermarking is predicated on the concept that the signal may carry several different watermarks at the identical time. The signal is also audio, pictures or video. Security issue in watermarking is because of enlargement of internet within the present paper the primary phase detailed description of watermarking has been on condition that data set are prepared on which watermarking technique are executed. In the second phase detailed working of the various techniques of image watermarking have to locate a selected watermarking technique which is able to provide appropriate ends up in term of PSNR and interval and various attacks are tested on images so implemented method must stand against various attacks. In final phase reverse process are executed to extract host and watermark image. There are many viable attacks. Spotting is an algorithm which is applied to the attacked signal to infusion the watermark from it. During the research work our main focus will be to enhance various critical paramters like PSNR and execution time so that better outcome can be attained.

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.

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