Coupled grouping and matching for sign and gesture recognition

Yang, Ruiduo; Sarkar, Sudeep

doi:10.1016/j.cviu.2008.09.005

Cited by 28 publications

(13 citation statements)

References 17 publications

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“…We can see that the best result for subjects 21-25 were 76% with 0.70 SIFT threshold, 80% with 0.70 SIFT threshold, 66% with 0.70 SIFT threshold, 68% with 0.65 and 0.70 SIFT threshold, and 62% with 0.75 SIFT threshold for the five subjects, respectively. Since the signers of this test set (subjects [21][22][23][24][25] were different signers from the training data set and the signature library, the results of this experiment provided low classification. Furthermore, when we used SIFT with the unconstrained system and complex natural backgrounds, the matched keypoints might be incorrectly matched, as shown Figure 3g.…”

Section: Resultsmentioning

confidence: 99%

“…This allows the system to be able to recognise hand sign words that have similar gestures. However, when we tested our algorithm with the test subject without any constraint on five signers (subjects [21][22][23][24][25], who were asked to stand in front of various complex backgrounds and could wear any shirt, the best correct classification rate in this case was around 70-80% on average.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

2017

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“…Wang et al [132] concatenated body centroid, hand displacement and relative depth level of hand to represent the hand gesture in each depth frame. Similarly, Yang et al [133] utilized groups of low-level image primitives such as region shape, proximity, or color to implicitly represent the hand as the salient object part of interest. The sequence of the frame-wise group primitives form the hand gesture feature in temporal domain without requiring a perfect hand segmentation.…”

Section: A Feature Representationmentioning

confidence: 99%

Survey on 3D Hand Gesture Recognition

Cheng

Yang

Liu

2016

IEEE Trans. Circuits Syst. Video Technol.

290

110

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3D hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 3D hand modeling, static hand gesture recognition, hand trajectory gesture recognition and continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

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“…Many researchers have been working on the recognition of various sign languages and gestures, but this research poses major difficulties due to the complexity on hand and body movements in sign language expression [1]- [3]. Recently several approaches to represent the motion information for the human action recognition in video have been reported.…”

Section: Introductionmentioning

confidence: 99%

Sign Language Recognition Using Motion History Volume and Hybrid Neural Networks

Kim¹,

Park²,

Lee³

2012

IJMLC

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Abstract-In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is introduced. We have defined a measure which reflects the relevance between the feature types and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.

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Coupled grouping and matching for sign and gesture recognition

Cited by 28 publications

References 17 publications

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

Survey on 3D Hand Gesture Recognition

Sign Language Recognition Using Motion History Volume and Hybrid Neural Networks

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