Hand Gesture Segmentation Method Based on YCbCr Color Space and K-Means Clustering

Zhang, Qiuyu; Lu, Junchi; Zhang, Mo-Yi; Huang, Duan; Lv, Lu

doi:10.14257/ijsip.2015.8.5.11

Cited by 18 publications

(3 citation statements)

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“…The segmented hand is then identified using a variety of techniques for extracting features and recognizing the segmented hand. One of the study articles by [ 4 ] was based on robust hand motion segmentation utilizing YCbCr color space and K-means clustering.…”

Section: Recognition Technologies Of Hand Gesturementioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

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Section: Recognition Technologies Of Hand Gesturementioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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“…The threshold values of YCbCr colour space are not fixed because of different skin colours; lighting conditions; and input device shadow effects. The different threshold values of YCbCr colour space were considered in the literature [14][15][16]. Thakur et al (2011) proposed skin colour model as combination of three colour spaces such as RGB, HSV and YCbCr called RGB_HS_CbCr [17].…”

Section: Related Workmentioning

confidence: 99%

Static and Dynamic Hand Gesture Recognition Using GIST and Linear Discriminant Analysis

The¹,

Yu²

2021

IJIES

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Hand gesture detection and recognition is a way to communicate between deaf-mute person and rest of society. In this study, the static sign and the dynamic sign recognition system based on hand gesture is presented. The dynamic sign gesture is more challenging than static sign gesture which contains the movement. The vision based sign language recognition using hand gesture consists of the following main steps: acquisition the sign data, detection the hand region, extraction the features and recognition. The data acquisition stage, the data acquired by using web camera without using any data gloves and special markers. In hand region detection process, this study proposed hybrid hand region detection process by combining CbCr channel from YCbCr colour space and motion detection by using mean filter background subtraction to definitely segment the hand region from the background. In hand feature extraction, the scale, shape, texture and orientation features extracted by using GIST (Generalized Search Tree), HOG (histogram of gradient) and HOG-LBP (Local Binary Pattern) methods. Finally, Quadratic Support Vector Machine (QSVM), Cubic Support Vector Machine (CSVM) and Linear Discriminant recognized the static and dynamic hand gesture. The recognition accuracy achieved 86.7% and 99.22 % acceptable accuracy on self-construct dynamic Myanmar sign word dataset and MUDB dataset.

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“…Utilizing the c-means clustering model by determining a random initial centroid [5], determine the distance between objects and normalize the data to improve the process of c-means clustering [6]. The c-means algorithm has been applied in some research, such as: (1) Hygiene : Clustering of the Parkinson'sdisease [ 7 ] [ 8 ] ,Obese management [9], Health care knowledge discovery [10]; (2) Clustering image : Satellite Image [11], Segmentation of white blood cells [12], Brain image segmentation [13], Content based image retrieval (CBIR) [14], Banana Image Segmentation [15], Hand gesture segmentation [16], Segmentation of fruits based on color features [17]; (3) Network science: Network partition [18], Wireless sensor networks [19]; (4) Academic science : Student careers [20], Predicting students Performance [21] ; (5) Customer satisfaction : Evaluate the cluster customers [22], Customer satisfaction in fast-food restaurant [23]; (6) Multimedia applications [24]; (7) Chemical oxygen demand [25]; (7) Approach to characterize road accident locations [26]; (8) Watershed classification [27]; (9) Wind speed [28]; (10) Tax based on cluster [29]; (11) Plagiarism detection System [30]; (12) Dictionary learning [31]; (13) Crime analysis [32]; (14) Connection oriented telecommunication data [33]; (15) Analyze Software Architecture [34]; (16) Prediction of atomic web services reliability [35] etc. It has shown that the cmean algorithm already implemented cases to solve the human problems…”

Section: Related Workmentioning

confidence: 99%

Clustering Analysis of Premier Research Fields

Terttiaavini¹,

Zamzam²,

Ramadhan³

et al. 2018

IJET

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The clusterization is one of methods which utilized to grouping a dataset which has a specific characteristics value. The processed data can be numerical or non-numerical data. Non-numeric data must be transformed first into numerical data. The case study in this study was to group research from six fields of science. The research data is non-numerical data is converted into the research contributions percentage in the science field. Utilized the c-means algorithm, the data was successfully grouped into three excellent research fields. The aim of the clustering is to know how many researchers in one cluster. Dataset is processed by utilizing the c-means algorithm to generated 3 clusters, they are an expeditious technology, entrepreneur and economic creative development, social engineering and strategic area infrastructure development. The data clustering result is presented in the graphic form by utilized the studio Rapidminer application.

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Hand Gesture Segmentation Method Based on YCbCr Color Space and K-Means Clustering

Cited by 18 publications

References 0 publications

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Static and Dynamic Hand Gesture Recognition Using GIST and Linear Discriminant Analysis

Clustering Analysis of Premier Research Fields

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