Large vocabulary sign language recognition based on fuzzy decision trees

Fang, Gaolin; Gao, Wen; Zhao, Debin

doi:10.1109/tsmca.2004.824852

Cited by 105 publications

(50 citation statements)

References 21 publications

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“…Fang et al [28] used a cascaded classifier that classified progressively one or two hands, hand shape and finally used a Self Organizing Feature Map (SOFM)/HMM to classify the words. The novelty of their approach was to allow multiple paths in the cascaded classifier to be taken, allowing for 'fuzziness'.…”

Section: Classification Methodsmentioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

176

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This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

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Section: Classification Methodsmentioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

176

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“…Unlike general gestures, sign language is highly structured so that it provides an appealing test bed for new ideas and algorithms before they are applied to gesture recognition. The recognition methods usually are mainly concerned with the difficulty presented by a large vocabulary sign language (as, e.g., in [10]) and how to reduce the recognition time without a great loss of accuracy (as, e.g., in [19]). Fuzzy methods, however, are worried mainly with representing the uncertainties involved in the process (as, e.g., in [22]), in other to increase the the quality of the results.…”

Section: Conclusion and Final Remarksmentioning

confidence: 99%

“…There is an extensive literature about methods and systems for gesture recognition [1,2,6,10,11,13,18,19,21] in general, and hand gesture recognition [7] in particular, such as, e.g. : systems for the recognition of 3-D and 2-D gestures captured by different devices (data gloves, cameras etc.)…”

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Recognition in an Interval Fuzzy Approach

Bedregal¹,

Dimuro²,

Costa³

2007

TEMA - Tendências Em Matemática Aplicada E Computacional

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Abstract. This paper introduces an interval fuzzy rule-based method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of hand gestures of the Brazilian Sign Language. To deal with the uncertainties in the data provided by the data glove, an approach based on interval fuzzy logic is used. The method uses the set of angles of finger joints for the classification of hand configurations, and classifications of segments of hand gestures for recognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment. Each gesture is characterized by its list of monotonic segments. The set of all lists of segments of a given set of gestures determine a set of finite automata able to recognize such gestures.

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“…Among several methods for gesture recognition, there are methods based on fuzzy logic and fuzzy sets, methods based on neural networks, hybrid neuro-fuzzy methods [11], fuzzy rule [12] and finite state machine [13] based methods, methods based on hidden Markov models [14] etc. In particular, considering methods for sign language recognition, some literature can be found related to fuzzy methods, such as, for example, fuzzy decision trees [15] and neuro-fuzzy systems [16].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule-Based Hand Gesture Recognition

Bedregal

Costa

Dimuro

IFIP International Federation for Information Processing

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Abstract. This paper introduces a fuzzy rule-based method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of some sample hand gestures of LIBRAS, the Brazilian Sign Language. The method uses the set of angles of finger joints for the classification of hand configurations, and classifications of segments of hand gestures for recognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment, sequences of hand configurations in which the variations of the angles of the finger joints have the same sign (non-increasing or non-decreasing). Each gesture is characterized by its list of monotonic segments. The set of all lists of segments of a given set of gestures determine a set of finite automata, which are able to recognize every such gesture.

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Large vocabulary sign language recognition based on fuzzy decision trees

Cited by 105 publications

References 21 publications

Sign Language Recognition

Sign Language Recognition

Hand Gesture Recognition in an Interval Fuzzy Approach

Fuzzy Rule-Based Hand Gesture Recognition

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