A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

Duan, Hongliang; Luo, Yang

doi:10.2991/iccsee.2013.90

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…rotation situation where the Viola-Jones face detection algorithm failed to detect face and nose regions in skin colour segmentation by online training thresholds procedure. Figure 8 illustrates the skin colour segmentation results using offline training thresholds in Cb-Cr chrominance components where the threshold values are calculated by (3) and from a number of skin colour samples taken from advocated dataset IBGHT, as depicted in Figures 3 and 4. Finally, skin feature was segmented into binary image using (4) where white pixels match the skin information.…”

Section: Advances In Multimediamentioning

confidence: 99%

“…In previous years, skin colour feature segmentation played a pivotal role in dynamic hand gesture segmentation since skin is invariant to hand scale changes and posture variations [2]. The existing body of studies on skin colour segmentation has suggested the use of colour spaces to perform human skin segmentation by particular skin colour threshold [3][4][5]. This is because utilising skin colour threshold approach can easily and simply segment the skin features including hand skin from the background to be used within dynamic hand gesture segmentation methods [6].…”

Section: Introductionmentioning

confidence: 99%

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Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

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Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive characteristics of skin colour and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin colour as a feature to segment dynamic hand gesture, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, this paper has proposed a skin segmentation scheme. This scheme includes two procedures for calculating generic threshold ranges in Cb-Cr colour space. The first procedure uses threshold values trained online from nose pixels of the face region. Meanwhile, the second procedure known as the offline training procedure uses thresholds trained out of skin samples and weighted equation. The experimental results showed that the proposed scheme achieved good performance in terms of efficiency and computation time.

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Section: Advances In Multimediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…[6]. In addition, common gesture feature extraction methods are also based on color information [9], gradient histogram [10] etc.. But these above gesture recognition methods are poor real-time, and susceptible to quite a few factors, including complex background, lighting, complexity of algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Gesture Trajectory Recognition Based on Key Frame Extraction and HMM

Zhang¹,

Lu²,

Zhang³

et al. 2015

IJSIP

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Aiming at changing high computational complexity, underdeveloped real time, low recognition rate of dynamic gesture recognition algorithms, this paper present a real-time dynamic gesture trajectory recognition method based on key frame extraction and HMM. Key frames are selected without keeping track of all the details of one dynamic gesture, which is based on difference degree between frames. The trajectory data stream, sorted by the time-warping algorithm, is used to construct the Hidden Markov Method model of dynamic gesture. Finally, optimal transition probabilities are employed to implement dynamic gesture recognition. The result of this experiment implies that this method has high robustness and real time. The average recognition rate of dynamic gesture (0~9) is up to 87.67%, and average time efficiency is 0.46s.

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A new wireless sensing approach for big images through image viewer control using hand gestures

Santra

Mali

Basu

et al. 2017

2017 Devices for Integrated Circuit (DevIC)

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A Method of Gesture Segmentation Based on Skin Color and Background Difference Method

Cited by 5 publications

References 4 publications

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

A Dynamic Gesture Trajectory Recognition Based on Key Frame Extraction and HMM

A new wireless sensing approach for big images through image viewer control using hand gestures

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