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Hits 1 – 20 of 33
1
Development of an American sign language game for deaf children
Valerie Henderson
;
Seungyon Lee
;
Helene Brashear
...
In: http://www.cc.gatech.edu/ccg/publications/idc05_henderson.pdf (2005)
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Development of an American sign language game for deaf children
Valerie Henderson
;
Seungyon Lee
;
Helene Brashear
...
In: http://www-static.cc.gatech.edu/~thad/p/030_30_AC/idc05_final.pdf (2005)
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3
User-centered development of a gesture-based American Sign Language game. Paper presented at the Instructional Technology and Education of the Deaf Symposium
Seungyon Lee
;
Valerie Henderson
;
Helene Brashear
...
In: http://www-static.cc.gatech.edu/~thad/p/030_30_AC/NTID05_0526_final.pdf (2005)
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4
Development of an American sign language game for deaf children
Valerie Henderson
;
Seungyon Lee
;
Helene Brashear
...
In: http://www.cc.gatech.edu/~brashear/pubs/IDC05.pdf (2005)
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5
User-centered development of a gesture-based American Sign Language game. Paper presented at the Instructional Technology and Education of the Deaf Symposium
Seungyon Lee
;
Valerie Henderson
;
Helene Brashear
...
In: http://www.rit.edu/ntid/vp/techsym/papers/2005/M2D.pdf (2005)
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6
Development of an American Sign Language game for deaf children
Valerie Henderson
;
Seungyon Lee
;
Helene Brashear
...
In: http://www-static.cc.gatech.edu/gvu/ccg/projects/copycat/p/idc05_final.pdf (2005)
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7
User-centered development of a gesture-based American Sign Language game. Paper presented at the Instructional Technology and Education of the Deaf Symposium
Seungyon Lee
;
Valerie Henderson
;
Helene Brashear
...
In: http://www-static.cc.gatech.edu/gvu/ccg/projects/copycat/p/NTID05_0526_final.pdf (2005)
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8
Towards a One-Way American Sign Language Translator
R. Martin Mcguire
;
Jose Hern
;
Thad Starner
...
In: http://wearables.cc.gatech.edu/publications/faceandgesture04_mcguire.pdf (2004)
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9
Towards a One-Way American Sign Language Translator
R. Martin Mcguire
;
Jose Hern
;
Thad Starner
...
In: http://www.cc.gatech.edu/~brashear/pubs/GESTURE2004.pdf (2004)
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10
Using Multiple Sensors for Mobile Sign Language Recognition
Helene Brashear
;
Thad Starner
In: http://wearables.cc.gatech.edu/publications/iswc2003-sign/iswc2003-sign.pdf (2003)
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11
Georgia Tech Gesture Toolkit: Supporting experiments in gesture recognition
Tracy Westeyn
;
Helene Brashear
;
Amin Atrash
...
In: http://www.cc.gatech.edu/~brashear/pubs/ICMI2003.pdf (2003)
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12
Using Multiple Sensors for Mobile Sign Language Recognition
Helene Brashear
;
Thad Starner
In: http://www1.cs.columbia.edu/graphics/courses/mobwear/resources/brashear-iswc03.pdf (2003)
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13
Using Multiple Sensors for Mobile Sign Language Recognition
Helene Brashear
;
Thad Starner
;
Paul Lukowicz
...
In: http://www.cc.gatech.edu/ccg/././publications/iswc2003-sign/iswc2003-sign.ps (2003)
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14
Georgia Tech Gesture Toolkit: Supporting Experiments in Gesture Recognition
Tracy Westeyn
;
Helene Brashear
;
Amin Atrash
...
In: http://www.cc.gatech.edu/ccg/publications/westeyn_ICMI2003.pdf (2003)
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15
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
Thad Starner
;
Joshua Weaver
;
Alex Pentland
In: http://www.cc.gatech.edu/~thad/p/journal/real-time-asl-recognition.pdf (1998)
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16
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
Thad Starner
;
Joshua Weaver
;
Alex Pentland
In: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-466.ps.Z (1998)
Abstract:
We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92% word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98% accuracy (97% with an unrestricted grammar). Both experiments use a 40 word lexicon. Categories: Gesture Recognition, Hidden Markov Models, Wearable Computers, Sign Language, Motion and Pattern Analysis. 1 Introduction While there are many different types of gestures, the most structured sets belong to the sign languages. In sign language, each gesture already has assigned meaning, and strong rules of context and grammar may be applied to make recognition tractable. American Sign Language (ASL) is the language of choice for most deaf in the United States. ASL uses approximately 6000 gestures for common words and finger spelling.
Keyword:
Categories
;
Gesture Recognition
;
Hidden Markov Models
;
Motion and Pattern Analysis
;
Sign Language
;
Wearable Computers
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.4893
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17
Real-Time American Sign Language Recognition from Video Using Hidden Markov Models
Thad Starner
;
Alex Pentland
In: http://www-static.cc.gatech.edu/~thad/p/031_10_SL/real-time-asl-recognition-from%20video-using-hmm-disab96.pdf (1998)
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18
Real-time american sign language recognition using desk and wearable computer based video
Thad Starner
;
Joshua Weaver
;
Alex Pentland
In: http://luthuli.cs.uiuc.edu/~daf/courses/Signals AI/Papers/HMMs/00735811.pdf (1998)
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19
A Wearable Computer Based American Sign Language Recognizer
Thad Starner
;
Joshua Weaver
;
Alex Pentland
In: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-425.ps.Z (1997)
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20
A Wearable Computer Based American Sign Language Recognizer
Thad Starner
;
Joshua Weaver
;
Alex Pentland
In: http://c2000.cc.gatech.edu/classes/cs8113c_99_spring/readings/starner.pdf (1997)
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