GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation

Nagendraswamy, H. S.; Kumara, B M Chethana; Chinmayi, R

doi:10.1007/978-3-319-26832-3_11

Cited by 8 publications

(10 citation statements)

References 26 publications

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“…The sign language recognition research works have been addressed at finger spelling level in [2,13,14,23,26,27], at word level [12,17,26] and at sentence level [4,16,17]. The techniques which gained importance due to their performance by research community are Ichetrichef moments [7], Gray level histogram [31], Sensor based glove technique [7,8,11,21], Hidden Morkov Models (HMM) [1], Hu moments and Electromyography (EMG) segmentation [1], Localized contour sequence [11], Size function [19], Transitionmovement [6], Moment based size function [9], Convex chain coding and Basic chain code [21], Fourier descriptors [23], Grassman Covariance Matrix (GCM) [33], Fusion of appearance based and 5DT glove based features [21], Sparse Observation (SO) description [29].…”

Section: Related Workmentioning

confidence: 99%

Indian sign language recognition: An approach based on fuzzy-symbolic data

Nagendraswamy

Kumara²,

Chinmayi³

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In this paper, the task of recognizing signs made by hearing impaired people at sentence level has been addressed. A novel method of detecting sign boundaries in a video of continuous signs has been proposed and extraction of spatial features to capture hand movements of a signer through fuzzy membership functions has been proposed. Frames of a given video of a sign are preprocessed to extract face and hand components of a signer. The centroids of the extracted components are exploited to extract spatial features. The concept of interval valued type symbolic data has been explored to capture variations in the same sign made by the different signers at different instances of time. A suitable symbolic similarity measure is studied to establish matching between reference and test signs and a simple nearest neighbor classifier is used to recognize an unknown sign as one among the known signs by specifying a desired level of threshold. An extensive experimentation is conducted on a significantly large corpus of Indian regional signs created by us during the course of our research work in order to evaluate the performance of the proposed system.

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Section: Related Workmentioning

confidence: 99%

Indian sign language recognition: An approach based on fuzzy-symbolic data

Nagendraswamy

Kumara²,

Chinmayi³

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…Several methods such as Elastic graph matching [44], modified census transformation [30], weighted Eigenspace size function [33], Hidden Markov Models (HMM) [3], weighted combination of geometric and appearance based features such as the area, gravity center [8], moment based size function [26], outline hand length and downscale intensity directly extracted from images in video frames [45] have been studied for user-independent hand posture recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Also, a practical continuous sign language recognition system needs to be signer independent. In order to address the problem of signer independent recognition system, it is necessary to device a robust representation system, which is tolerant to signer variations and yield good generalization; however, this has not received much attention in the literature except the work reported in [28,45].…”

Section: Related Workmentioning

confidence: 99%

Sign Energy Images for Recognition of Sign Language at Sentence Level

Kumara¹,

Nagendraswamy²

2016

IJCA

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In this paper, the task of sign language recognition at sentence level is addressed. The idea of Sign Energy Image (SEI) and a method of extracting Fuzzy-Gaussian Local Binary Pattern (FzGLBP) features from SEI to characterize the sign are explored. The suitability of interval valued type symbolic data for efficient representation of signs in the knowledgebase is studied. A Chi-square proximity measure is used to establish matching between reference and test signs. A simple nearest neighbor classification technique is used for recognizing signs. Extensive experiments are conducted to study the efficacy of the proposed system. A data base of signs called UoM-ISL is created for experimental analysis.

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“…The research works reported for sign language recognition have addressed the task at finger spelling level [2,12,13,21,24,25], at word level [11,17,24] and at sentence level [4,15,16]. Some of the techniques proposed by the research community, which gained importance due to their performance are Ichetrichef moments [6], Gray level histogram [29], Sensor based glove technique [6,7,10,17], Hidden Morkov Models (HMM) [1], Hu moments and Electromyography (EMG) segmentation [1], Localized contour sequence [10], Size function [17], Transition-movement [5], Moment based size function [8], Convex chain coding and Basic chain code [28], Fourier descriptors [22], Grassman Covariance Matrix (GCM) [31], Fusion of appearance based and 5DT glove based features [19], Sparse Observation (SO) description [27].…”

Section: Related Workmentioning

confidence: 99%

“…Since signs used by hearing impaired people are very abstract, the sign language recognition based on fingerspelling or word seems to be cumbersome and not effective. With this observation, recently only two attempts were reported to address the problem at sentence level [4,15,16]. Therefore, there is scope for many more attempts in this direction.…”

Section: Related Workmentioning

confidence: 99%

Spatial Relationship Based Features for Indian Sign Language Recognition

2016

IJCCIE

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Abstract-In this paper, the task of recognizing signs made by hearing impaired people at sentence level has been addressed. A novel method of extracting spatial features to capture hand movements of a signer has been proposed. Frames of a given video of a sign are preprocessed to extract face and hand components of a signer. The local centroids of the extracted components along with the global centroid are exploited to extract spatial features. The concept of interval valued type symbolic data has been explored to capture variations in the same sign made by the different signers at different instances of time. A suitable symbolic similarity measure is studied to establish matching between test and reference signs and a simple nearest neighbour classifier is used to recognize an unknown sign as one among the known signs by specifying a desired level of threshold. An extensive experimentation is conducted on a considerably large database of signs created by us during the course of research work in order to evaluate the performance of the proposed system. Index Terms-Hearing impaired, Key-frame, Sign language and Symbolic data.

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GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation

Cited by 8 publications

References 26 publications

Indian sign language recognition: An approach based on fuzzy-symbolic data

Indian sign language recognition: An approach based on fuzzy-symbolic data

Sign Energy Images for Recognition of Sign Language at Sentence Level

Spatial Relationship Based Features for Indian Sign Language Recognition

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