Real Time Hand Gesture Recognition Including Hand Segmentation and Tracking

Coogan, Thomas A.; Awad, George; Han, Junwei; Sutherland, Alistair

doi:10.1007/11919476_50

Cited by 25 publications

(10 citation statements)

References 10 publications

(11 reference statements)

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“…The processing rate of the webcam is 30 frames per second. The average execution speed is faster than the speed in [9] which is 10 frames per second.…”

Section: Performancementioning

confidence: 90%

“…Hand segmentation methods based on skin color have been used as efficient approaches in the previous research [6,9,10] on the premise that human skin color is distinct from the background color. An efficient skin-color segmentation method based on HSV (Hue, Saturation and Value) color space is implemented in our system because different skin color has a similar range in the HSV color space [10].…”

Section: Hand Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Vision-based Hand Gesture Interaction Using Particle Filter, Principle Component Analysis and Transition Network

Wang¹,

Zhang²,

Wang³

et al. 2014

J. Inf. Comput. Sci.

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Vision-based human-computer interaction is becoming important nowadays. It offers natural interaction with computers and frees users from mechanical interaction devices, which is favourable especially for wearable computers. This paper presents a human-computer interaction system based on a conventional webcam and hand gesture recognition. This interaction system works in real time and enables users to control a computer cursor with hand motions and gestures instead of a mouse. Five hand gestures are designed on behalf of five mouse operations: moving, left click, left-double click, right click and no-action. An algorithm based on Particle Filter is used for tracking the hand position. PCA-based feature selection is used for recognizing the hand gestures. A transition network is also employed for improving the accuracy and reliability of the interaction system. This interaction system shows good performance in the recognition and interaction test.

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“…The processing rate of the webcam is 30 frames per second. The average execution speed is faster than the speed in [9] which is 10 frames per second.…”

Section: Performancementioning

confidence: 90%

Section: Hand Segmentationmentioning

confidence: 99%

Vision-based Hand Gesture Interaction Using Particle Filter, Principle Component Analysis and Transition Network

Wang¹,

Zhang²,

Wang³

et al. 2014

J. Inf. Comput. Sci.

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show abstract

“…If one of these gestures is shown by the user and recognized by the system, it can be immediately transferred to the avatar on the receiving side. In contrast to template based approaches, e.g., [31], we applied a method based on evaluation of a specific distance function derived from the contour of the hand segment. This approach has been applied successfully in other systems [32], but in contrast to that proposal the gesture can be shown quite naturally without posing it on a specific flat panel.…”

Section: E Gesture Recognitionmentioning

confidence: 99%

Real-Time Vision and Speech Driven Avatars for Multimedia Applications

Schreer

Englert

Eisert

et al. 2008

IEEE Trans. Multimedia

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Recent progress in advanced video communication services and multimedia applications is grounded on novel human machine interfaces, improved usability, and user friendliness driven by user centric research and development. In this paper, we describe a complete system concept and algorithmic details of an example application within this area. The key features of the system are vision and speech based interfaces, which are used to animate an avatar for an audio-visual representation of a communication partner. The system is applied in two application scenarios, namely video chat and customer care services. Both applications are mass-market oriented and therefore careful design and development of robust and supporting user interfaces are required. The presented approach is integrated into a complete real-time prototype system, which is permanently demonstrated in the showcase at the head quarter

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“…The hand trajectory is utilized as a feature vector for DTW [2], hand contour [3], combination of color, motion and hand position is utilized for hand gesture recognition [4,7]. The hand skin color information is used for extracting the histogram [5] .The histogram of the local direction of edges in an image contributes an important feature [9,10].…”

Section: Feature Extractionmentioning

confidence: 99%

Recognition of Isolated Indian Sign Language Gesture in Real Time

Nandy

Prasad

Mondal

et al. 2010

Communications in Computer and Information Science

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Abstract. Indian Sign Language (ISL) consists of static as well as dynamic hand gestures for communication among deaf and dumb persons. Most of the ISL gestures are produced using both hands. A video database is created and utilized which contains several videos, for a large number of signs. Direction histogram is the feature used for classification due to its appeal for illumination and orientation invariance. Two different approaches utilized for recognition are Euclidean distance and K-nearest neighbor metrics.

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Real Time Hand Gesture Recognition Including Hand Segmentation and Tracking

Cited by 25 publications

References 10 publications

Vision-based Hand Gesture Interaction Using Particle Filter, Principle Component Analysis and Transition Network

Vision-based Hand Gesture Interaction Using Particle Filter, Principle Component Analysis and Transition Network

Real-Time Vision and Speech Driven Avatars for Multimedia Applications

Recognition of Isolated Indian Sign Language Gesture in Real Time

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