Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm

Tobely, Tarek El.; Yoshiki, Yuichiro; Tsuda, Ryuichi; Tsuruta, Naoyuki; Amamiya, Makoto

doi:10.1007/3-540-40992-0_19

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Typical techniques are pattern matching (Tamura and Kawasaki, 1988), feature extraction (Imagawa et al, 2000), model matching (Shimada et al, 1995), and interactive learning (Lee and Xu, 1996). Tobely et al (2000) applied a randomized self-organizing map algorithm for dynamic recognition of hand gestures with normal video rates. Hidden Markov functions have also been applied to recognize hand gestures (Nam and Wohn, 1996).…”

Section: Research In Manipulator Indicative and Descriptive Functionmentioning

confidence: 99%

Using Hand Motions in Conceptual Shape Design: Theories, Methods and Tools

Varga

2008

Product Engineering

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Section: Research In Manipulator Indicative and Descriptive Functionmentioning

confidence: 99%

Using Hand Motions in Conceptual Shape Design: Theories, Methods and Tools

Varga

2008

Product Engineering

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“…Hand gesture recognition based on monocular vision has become a common gesture recognition method. In 2000, Tare et al proposed a random organization mapping algorithm to track and identify the gesture, which effectively reduced the computation time and improved the recognition accuracy [7]. By recognizing the texture features of the key parts of the human hand, Bhuyan et al can recognize the bending motion of the finger by using a monocular camera [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Gesture recognition based on binocular vision

Zheng

et al. 2018

Cluster Comput

127

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A convenient and effective binocular vision system is set up. Gesture information can be accurately extract from the complex environment with the system. The template calibration method is used to calibrate the binocular camera and the parameters of the camera are accurately obtained. In the phase of stereo matching, the BM algorithm is used to quickly and accurately match the images of the left and right cameras to get the parallax of the measured gesture. Combined with triangulation principle, resulting in a more dense depth map. Finally, the depth information is remapped to the original color image to realize three-dimensional reconstruction and three-dimensional cloud image generation. According to the cloud image information, it can be judged that the binocular vision system can effectively segment the gesture from the complex background.

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Comprehending a Hand Motion Language in Shape Conceptualization

Horváth

Tromp

Daalhuizen

2003

Volume 1: 23rd Computers and Information in Engineering Conference, Parts a and B

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This paper reports on a study concerning the comprehension of an experimental hand motion language in shape conceptualization. Hand motion is regarded as a prospective input mechanism for computer aided conceptual design systems for initial shape design of consumer durables. Our hand motion language has been developed based on the analysis of the (a) information necessary to describe shape concepts, (b) descriptive and indicative capabilities of human hands, and (c) cognitive and perceptive aspects of processing hand motions. This language was used in designed experiments to describe simple, compound and hybrid shapes for designers and tested with respect to comprehensibility. The subjects were asked to reconstruct the presented shapes by sketching on paper. Comprehension of the hand motion language has been evaluated in terms of fidelity and efficiency. Fidelity was expressed in terms of the number of deviations of the sketches of the test shapes from the existing CAD models. Efficiency was expressed as the proportion of the time needed to understand the test shapes communicated by hand motions and the time needed for the presentation. The results clearly indicate the potentials of a HML in shape conceptualization. In addition, the experiments revealed several new issues related to the application of the hand motion language in a multi-modal interface. The most important one is the need of chunking of the hand motion sequences in a way that enables the computer system to reliably reconstruct shapes and the designers to understand the formation of shapes on a higher semantic level. These issues will be addressed in our future research.

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Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm

Cited by 3 publications

References 4 publications

Using Hand Motions in Conceptual Shape Design: Theories, Methods and Tools

Using Hand Motions in Conceptual Shape Design: Theories, Methods and Tools

Gesture recognition based on binocular vision

Comprehending a Hand Motion Language in Shape Conceptualization

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