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US Patent 10037458 Automated sign language recognition

Patent 10037458 was granted and assigned to King Fahd University of Petroleum and Minerals on July, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
King Fahd University of Petroleum and Minerals
King Fahd University of Petroleum and Minerals
1
Current Assignee
King Fahd University of Petroleum and Minerals
King Fahd University of Petroleum and Minerals
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
100374581
Patent Inventor Names
Ala Addin Sidig1
Sabri A. Mahmoud1
Date of Patent
July 31, 2018
1
Patent Application Number
155843611
Date Filed
May 2, 2017
1
Patent Citations Received
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US Patent 12002236 Automated gesture identification using neural networks
2
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US Patent 11055521 Real-time gesture recognition method and apparatus
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US Patent 11756205 Methods, devices, apparatuses and storage media of detecting correlated objects involved in images
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US Patent 11804077 Generic gesture detecting method and generic gesture detecting device
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US Patent 11928592 Visual sign language translation training device and method
4
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US Patent 11954904 Real-time gesture recognition method and apparatus
5
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US Patent 10311294 Motion recognition via a two-dimensional symbol having multiple ideograms contained therein
1
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US Patent 10289903 Visual sign language translation training device and method
...
Patent Primary Examiner
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Michael Colucci
1
Patent abstract

A sign language recognizer is configured to detect interest points in an extracted sign language feature, wherein the interest points are localized in space and time in each image acquired from a plurality of frames of a sign language video; apply a filter to determine one or more extrema of a central region of the interest points; associate features with each interest point using a neighboring pixel function; cluster a group of extracted sign language features from the images based on a similarity between the extracted sign language features; represent each image by a histogram of visual words corresponding to the respective image to generate a code book; train a classifier to classify each extracted sign language feature using the code book; detect a posture in each frame of the sign language video using the trained classifier; and construct a sign gesture based on the detected postures.

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