Partly-hidden Markov model and its application to gesture recognition

Kobayashi, Tetsuo; Haruyama, S.

doi:10.1109/icassp.1997.595443

Cited by 32 publications

(21 citation statements)

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“…Meanwhile, the best average classification rate of the signer-independent cases (subjects [16][17][18][19][20] was 79.90%. Again, the greater the number of training subjects, the more accurate the system.…”

Section: Resultsmentioning

confidence: 99%

“…From the blind test results from the training process with 1a-15a, as shown in Figure 10, we can see that the best average classification rates for the signer-dependent cases (subjects 1-5) was 90.99%, whereas that of the signer semi-dependent cases (subjects 6-15) was 85.14%. Meanwhile, the best average classification rate of the signer-independent cases (subjects [16][17][18][19][20] was 79.90%. Again, the greater the number of training subjects, the more accurate the system.…”

Section: Resultsmentioning

confidence: 99%

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A Novel String Grammar Unsupervised Possibilistic C-Medians Algorithm for Sign Language Translation Systems

2017

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Abstract:Sign language is a basic method for solving communication problems between deaf and hearing people. In order to communicate, deaf and hearing people normally use hand gestures, which include a combination of hand positioning, hand shapes, and hand movements. Thai Sign Language is the communication method for Thai hearing-impaired people. Our objective is to improve the dynamic Thai Sign Language translation method with a video captioning technique that does not require prior hand region detection and segmentation through using the Scale Invariant Feature Transform (SIFT) method and the String Grammar Unsupervised Possibilistic C-Medians (sgUPCMed) algorithm. This work is the first to propose the sgUPCMed algorithm to cope with the unsupervised generation of multiple prototypes in the possibilistic sense for string data. In our experiments, the Thai Sign Language data set (10 isolated sign language words) was collected from 25 subjects. The best average result within the constrained environment of the blind test data sets of signer-dependent cases was 89-91%, and the successful rate of signer semi-independent cases was 81-85%, on average. For the blind test data sets of signer-independent cases, the best average classification rate was 77-80%. The average result of the system without a constrained environment was around 62-80% for the signer-independent experiments. To show that the proposed algorithm can be implemented in other sign languages, the American sign language (RWTH-BOSTON-50) data set, which consists of 31 isolated American Sign Language words, is also used in the experiment. The system provides 88.56% and 91.35% results on the validation set alone, and for both the training and validation sets, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Novel String Grammar Unsupervised Possibilistic C-Medians Algorithm for Sign Language Translation Systems

2017

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“…A Motion Energy Image (MEI) using (6, 4 ) is constructed for 4 (γ = 4 in (6)) previous levels and this data is merged with the color data by bit-wise OR of the color localization data ( 6 ). By the end of this step we get data which represents the motion and color data in a single image ( 7 ). By finding connected components of the motion and color data we get a ROI from which to prepare a feature vector.…”

Section: B Motion Processmentioning

confidence: 99%

“…By finding connected components of the motion and color data we get a ROI from which to prepare a feature vector. We call this image the Motion Color Image (MCI) ( 7 ). The red bounding box is the full bounding box of the motion color data.…”

Section: B Motion Processmentioning

confidence: 99%

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A Real-Time Framework for Vision based Human Robot Interaction

Singh

Seth

Desai

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body dynamic gesture recognition system for such robots. The said framework works in real world lighting conditions with complex backgrounds, and can handle intermittent motion of the camera. We present here a novel way in which the Motion History Image (MHI) and the Motion Energy Image (MEI) is built. We propose a robust combining of the motion and color cues and we call this image as Motion Color Image (MCI). The input signal is captured by using monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. Gestures are first learned offline and then matched to the temporal data generated online in real time. We have tested this on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%. We have partially implemented and testing the system for Sony's Aibo robot dog using Remote Framework (RFW) SDK by Sony.

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Visual Sensing and Gesture Interactions

Bhowmik

2014

Interactive Displays

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Partly-hidden Markov model and its application to gesture recognition

Cited by 32 publications

References 1 publication

A Novel String Grammar Unsupervised Possibilistic C-Medians Algorithm for Sign Language Translation Systems

A Novel String Grammar Unsupervised Possibilistic C-Medians Algorithm for Sign Language Translation Systems

A Real-Time Framework for Vision based Human Robot Interaction

Visual Sensing and Gesture Interactions

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