Glove-Talk: a neural network interface between a data-glove and a speech synthesizer

Fels, Sidney; Hinton, Geoffrey E.

doi:10.1109/72.182690

Cited by 285 publications

(88 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First attempts to solve this problem resulted in mechanical devices that directly measure hand and/or arm joint angles and spatial position. This group is best represented by the so-called glove-based devices [9], [32], [88], [70], [101]. Glove-based gestural interfaces require the user to wear a cumbersome device, and generally carry a load of cables that connect the device to a computer.…”

Section: Introductionmentioning

confidence: 99%

Visual interpretation of hand gestures for human-computer interaction: a review

Pavlović

Sharma

Huang

1997

IEEE Trans. Pattern Anal. Machine Intell.

1,439

637

View full text Add to dashboard Cite

Abstract-The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient "purposive" approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of humancomputer interaction.

show abstract

Section: Introductionmentioning

confidence: 99%

Visual interpretation of hand gestures for human-computer interaction: a review

Pavlović

Sharma

Huang

1997

IEEE Trans. Pattern Anal. Machine Intell.

1,439

637

View full text Add to dashboard Cite

show abstract

“…Traditional training algorithms, such as the back-propagation algorithm (Rumelhart et al, 1986) and the Levenberg-Marquardt algorithm (Levenberg, 1944;Marquardt, 1963;Hagan and Menhaj, 1994), have been successfully applied to train neural networks (Hinton, 1989;Lang et al, 1990;Fels and Hinton, 1993). The former algorithm is based on gradient descent and approximates the error of the network with a first-order expression, whereas the latter algorithm is based on the Newton method and approximates the error of the network with a second-order expression.…”

Section: Training Artificial Neural Networkmentioning

confidence: 99%

Training neural networks with ant colony optimization algorithms for pattern classification

Mavrovouniotis

Yang

2014

Soft Comput

View full text Add to dashboard Cite

Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techniques, are used to determine the connection weights of the feed-forward neural network. However, gradient descent techniques often get trapped in local optima of the search landscape. To address this issue, an ant colony optimization (ACO) algorithm is applied to train feed-forward neural networks for pattern classification in this paper. In addition, the ACO training algorithm is hybridized with gradient descent training. Both standalone and hybrid ACO training algorithms are evaluated on several benchmark pattern classification problems, and compared with other swarm intelligence, evolutionary and traditional training algorithms. The experimental results show the efficiency of the proposed ACO training algorithms for feed-forward neural networks for pattern classification.

show abstract

“…Once the gloves have captured hand pose data, gestures can be recognized using a number of different techniques. Neural network approaches or statistical template-matching approaches are commonly used to identify static hand posses [Fels, 1993]. Time dependent neural network and Hidden Markov Model (HMM) are commonly used for dynamic gesture recognition [Lee, 1996].…”

Section: Glove-based Approachesmentioning

confidence: 99%

Face and Gesture Recognition for Human-Robot Interaction

Ueno¹

2007

Face Recognition

View full text Add to dashboard Cite

Glove-Talk: a neural network interface between a data-glove and a speech synthesizer

Cited by 285 publications

References 3 publications

Visual interpretation of hand gestures for human-computer interaction: a review

Visual interpretation of hand gestures for human-computer interaction: a review

Training neural networks with ant colony optimization algorithms for pattern classification

Face and Gesture Recognition for Human-Robot Interaction

Contact Info

Product

Resources

About