American sign language (ASL) recognition based on Hough transform and neural networks

Qutaishat, M. A.; Habeeb, Moussa; Takruri, Bayan; Al-Malik, Hiba Abed

doi:10.1016/j.eswa.2005.11.018

Cited by 104 publications

(35 citation statements)

References 20 publications

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“…Another research was carried out to classify sign language ASLs using Hough transform and neural networks [15]. ASL is able to represent simple words in simple handwritten letters such as A to Z in a single hand shape, analyzing hand shapes through images, and studying what it means in ASL.…”

Section: Figure 2 Hand Center Point and Contour Detectionmentioning

confidence: 99%

A Study of Partial Image Classification of Vehicles Using Finger Gestures

Jeong¹,

Lee²,

Son³

2018

IJGDC

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Section: Figure 2 Hand Center Point and Contour Detectionmentioning

confidence: 99%

A Study of Partial Image Classification of Vehicles Using Finger Gestures

Jeong¹,

Lee²,

Son³

2018

IJGDC

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“…The decision whether the welding laser optics center is aligned with the center of the reference point is made by the dedicated software by implementing a circular Hough transform [24] to [26] on the captured laser optics video camera image. The task of the Hough transform method is to search for objects of different shapes (lines, circles, ellipse) in an image by a voting approach in parameter space.…”

Section: Robot Self-calibration Of the New Welding Laser Optics Toolmentioning

confidence: 99%

Robust Visual Touch-Up Calibration Method in Robot Laser Spot Welding Application

Rejc

Munih

2016

SV-JME

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“…While in [19] the Canny edges are further processed by Hough transformation in order to conclude into the gesture features. Simpler but very effective, in [21] the authors utilize the difference between the detected (strong & weak) edges as features.…”

Section: Related Workmentioning

confidence: 99%

“…Non-manual features are related to arms, the body and the facial expressions. Finally, finger-spelling that corresponds to letters and words in natural languages [19]. Specifically the non manual features are used to both individually form part of a sign or support other signs and modify their bearing meaning [26].…”

Section: Introductionmentioning

confidence: 99%

Static Gesture Recognition Combining Graph and Appearance Features

Avraam¹

2014

IJARAI

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Abstract-In this paper we propose the combination of graphbased characteristics and appearance-based descriptors such as detected edges for modeling static gestures. Initially we convolve the original image with a Gaussian kernel and blur the image. Canny edges are then extracted. The blurring is performed in order to enhance some characteristics in the image that are crucial for the topology of the gesture (especially when the fingers are overlapping). There are a large number of properties that can describe a graph, one of which is the adjacency matrix that describes the topology of the graph itself. We approximate the topology of the hand utilizing Neural Gas with Competitive Hebbian Learning, generating a graph. From the graph we extract the Laplacian matrix and calculate its spectrum. Both canny edges and Laplacian spectrum are used as features. As a classifier we employ Linear Discriminant Analysis with Bayes' Rule. We apply our method on a published American Sign Language dataset (ten classes) and the results are very promising and further study of this approach is imminent from the authors.

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American sign language (ASL) recognition based on Hough transform and neural networks

Cited by 104 publications

References 20 publications

A Study of Partial Image Classification of Vehicles Using Finger Gestures

A Study of Partial Image Classification of Vehicles Using Finger Gestures

Robust Visual Touch-Up Calibration Method in Robot Laser Spot Welding Application

Static Gesture Recognition Combining Graph and Appearance Features

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