Hand Gesture Recognition by Thinning Method

Rokade, Rajeshree S.; Doye, Dharmpal D.; Kokare, Manesh

doi:10.1109/icdip.2009.73

Cited by 42 publications

(14 citation statements)

References 6 publications

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“…Results in the output image as a gray scale image; next step is to convert this grey scale image to binary which is done by selecting the threshold using the Otsu's method. Thus the binary is then thinned [26]. While undergoing this procedure, noise and undesirable segments added to it.…”

Section: Ivmethods Of Hand Gesture Recognition Systemmentioning

confidence: 99%

A Review on Hand Gesture Recognition System

Sonkusare¹,

Chopade²,

Sor³

et al. 2015

2015 International Conference on Computing Communication Control and Automation

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From the ancient age, gesture was the first mode of communication, after the evolution of human civilization they developed the verbal communication, but still non-verbal communication is equally significant. Such non-verbal communication is not only used for physically challenged person but also it can be efficiently used for various applications such as 3D gaming, aviation, surveying, etc. This is the best method to interact with computer without any peripheral devices. Many Researchers are still developing robust and efficient new hand gesture recognition techniques. The major steps associated while designing the system are: data acquisition, segmentation and tracking, feature extraction and gesture recognition. There are different methodologies associated with several substeps present at each step. A various segmentation and Tracking, feature extraction and recognition techniques are studied and analyzed. This paper reviews the comparative study of various hand gesture recognition techniques which are presented up-till now.

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Section: Ivmethods Of Hand Gesture Recognition Systemmentioning

confidence: 99%

A Review on Hand Gesture Recognition System

Sonkusare¹,

Chopade²,

Sor³

et al. 2015

2015 International Conference on Computing Communication Control and Automation

View full text Add to dashboard Cite

show abstract

“…For human skin colour, the Cb range is 80-105, and the Cr range is 130-165 [3]. If the values of both Cb and Cr for any pixel are in the above ranges, the pixels are converted to 0; otherwise, the pixel is converted to 1.…”

Section: Hand Segmentation Algorithmmentioning

confidence: 99%

Spelled sign word recognition using key frame

Rokade

Doye

2015

IET Image Processing

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In this study, the authors present a new system for sign language hand gesture recognition. Using video input, the system can recognise any spelled word or alphabetic sequence signed in American Sign Language. The three main steps in the recognition process include detection of the region of interest (the hands), detection of key frames and recognition of gestures from these key frames. The proposed segmentation algorithm distinguishes regions of interest from both uniform and non-uniform backgrounds with an efficiency of 95%. The proposed key frame detection algorithm achieves an efficiency of 96.50%. A rotation-invariant algorithm for feature extraction is additionally proposed, which provides an overall gesture recognition efficiency of 84.2%.

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“…There are four main problems in most existing methods based on vision [2][3][4][5]: 1. Static background; 2.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Gesture Recognition Referring to Human Structure

Zhang

Liang

et al. 2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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Abstract. This article proposed a new method which introduces human body structure into gesture recognition besides the traditional vision information. By establishing the target's skeleton model, the system could rule out background interference quickly from the real-time frames, then locate the target's joints accurately, and track the gestures automatically. This method applies a common monocular webcam for image input instead of expensive depth cameras or complex binocular cameras. It can build a vivid human model and simulate the target's gestures well. After a modified KNN classification which has self-learning ability, the relationships between these joints are analyzed, and the corresponding postures and movements are identified. This method avoids global scanning in each frame and simplifies the classification, thus the computation decreases sharply. The results show that the method has a good tolerance of complex background, it can solve the target missing well.

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Hand Gesture Recognition by Thinning Method

Cited by 42 publications

References 6 publications

A Review on Hand Gesture Recognition System

A Review on Hand Gesture Recognition System

Spelled sign word recognition using key frame

Vision-Based Gesture Recognition Referring to Human Structure

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