Recognition of gestures in Arabic sign language using neuro-fuzzy systems

Al-Jarrah, Omar; Halawani, Alaa

doi:10.1016/s0004-3702(01)00141-2

Cited by 178 publications

(109 citation statements)

References 8 publications

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“…Most of the sign recognition systems are glove based because finger positions and orientations are easily detectable through it. The system proposed by Al-Jarrah & Alaa (2001) uses Cyber Gloves while that of Kadous (1996) needs Power Gloves. The approach based on Data Gloves for FSL (French Sign Language) Yang & Lee (2010) has 83% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Image based recognition of Pakistan sign language

Raees¹,

Ullah²,

Rahman³

et al. 2016

J Engin Res

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Section: Literature Reviewmentioning

confidence: 99%

Image based recognition of Pakistan sign language

Raees¹,

Ullah²,

Rahman³

et al. 2016

J Engin Res

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“…Defining appropriate membership functions (fuzzy sets) which best fit the data set is an important task that is done by subtractive clustering algorithm in Takagi and Sugeno (1985) FIS. Subtractive clustering is an effective approach to estimate the number of fuzzy clusters and cluster centers in Takagi-Sugeno fuzzy inference system (Jarrah and Halawani 2001). Subtractive clustering is governed by a design parameter, called clustering radius.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…A large cluster radius yields a few large clusters in data (Chiu 1994). More details about fuzzy logic and subtractive clustering are available in works by Jarrah and Halawani (2001), Chiu (1994), and Mohaghegh (2000). …”

Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

Zargar

Bagheripour

Asoodeh

2014

J Petrol Explor Prod Technol

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Oil formation volume factor (FVF) refers to the change in oil volume between reservoir and standard conditions at surface. It is a crucial oil property which is governed by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. This parameter plays a trivial role in petroleum reservoir and production calculations. Accurate determination of oil FVF is restricted by limitations on reliable sampling and high cost and time-consumption associated with laboratory experiments. Furthermore, available empirical correlations do not have satisfying generalization and accuracy owing to being calibrated on specific oil samples. Therefore, this study offers a Takagi-Sugeno (TS) fuzzy logic model for estimating oil FVF for the purpose of developing a precise model calibrated on regional Iranian oil using 367 training samples. TS fuzzy model utilizes subtractive clustering approach for determining number of rules and clusters. Evaluation of constructed fuzzy logic using 108 unseen test data points indicated achievement of fuzzy logic in prediction of oil FVF.

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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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Recognition of gestures in Arabic sign language using neuro-fuzzy systems

Cited by 178 publications

References 8 publications

Image based recognition of Pakistan sign language

Image based recognition of Pakistan sign language

Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release

Fuzzy Rule-Based Hand Gesture Recognition

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