Sign Language Recognition Based on Position and Movement Using Multi-Stream HMM

Masaru, Maebatake; Suzuki, Iori; Nishida, Masafumi; Horiuchi, Yasuo; Kuroiwa, Shingo

doi:10.1109/isuc.2008.56

Cited by 26 publications

(9 citation statements)

References 5 publications

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“…В другом случае используются данные электромиографии. Так, прибор Polhemus FASTRAK [4] предназначен для отслеживания частей тела человека в режиме реального времени по 6 степеням свободы (англ. 6DOF) с минимальной задержкой.…”

Section: определение жеста при помощи сенсорной гарнитуры/перчаткиunclassified

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Ryumin¹,

Kagirov²

2021

Manned Spaceflight

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In this paper, hardware and software solutions addressed to automatic gesture recognition are considered. Trends in image analysis in the current computer vision-based approaches are analysed. Each of the considered approaches was addressed, in order to reveal their advantages and drawbacks. Research papers on the usability of gesture interfaces were reviewed. It was revealed that sensor-based systems, being quite accurate and demonstrating high speed of recognition, have limited application due to the specificity of devices (gloves, suit) and their relatively narrow distribution. At the same time, computer vision-based approaches can be successfully applied only when problems of occlusions and datasets are solved. The results obtained can be used for designing training systems.

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Section: определение жеста при помощи сенсорной гарнитуры/перчаткиunclassified

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Ryumin¹,

Kagirov²

2021

Manned Spaceflight

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“…Depending on this angle fingers are identified. [5] The count of fingers that are active is determined by the midpoints that are plotted. After fingers are identified, the gestures also be recognized by the set of fingers obtained.…”

Section: C) Classification Of Gesturesmentioning

confidence: 99%

Hand Gesture Method to Speech Conversion using Image Segmentation and Feature Extraction Algorithm

Nagajyothi*,

Srilatha,

Jyothi

2019

IJRTE

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Hand gestures are used by the speech impaired persons to easily communicate with others. Hand gesture is a visual language, different from spoken language but serves as the same purpose of spoken language. Image segmentation and feature extraction algorithms are used to recognize the hand gestures of the deaf people .In this paper the proposed system will not only recognize the hand gestures but it will translate into suitable messages by using skin color segmentation. With the help of speakers speech playback is possible in the MATLAB.

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“…Many researches on hand gesture recognition have been implemented recently. For example, the approach in [1] was proposed for identifying sign language using a multi-stream HMM technique combined with weighted information of hand position and movement. Another system was developed using skin color segmentation and artificia neural network [2].…”

Section: Related Workmentioning

confidence: 99%

Geometry-based Dynamic Hand Gesture Recognition

Huynh

Meunier³

2015

JST

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Hand gestures play an important role in communication in the hard-of-hearing community. They are used to convey information instead of words. Besides, a system which is developed to identify gestures can be also used for human-computer interaction. In this paper, we propose a vision-based approach for recognizing dynamic hand gestures. Our processing consists of three main stages: pre-processing, feature extraction and recognition. The first stage involves two sub-stages: segmentation which locates the hand and extracts the corresponding silhouette using color information; separation that removes the arm based on geometrical properties. Some characteristics which describe the hand posture are then extracted. Finally, the recognition is performed using two popular algorithms, which are Dynamic Time Warping and Hidden Markov Model. The experiment is conducted on SKIG dataset with a comparison of classification accuracies corresponding to the two mentioned methods.

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Sign Language Recognition Based on Position and Movement Using Multi-Stream HMM

Cited by 26 publications

References 5 publications

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Hand Gesture Method to Speech Conversion using Image Segmentation and Feature Extraction Algorithm

Geometry-based Dynamic Hand Gesture Recognition

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