A learning-based prediction-and-verification segmentation scheme for hand sign image sequence

We present a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient. The system was...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 21; no. 8; pp. 798 - 804
Main Authors Cui, Y., Weng, J.
Format Journal Article
LanguageEnglish
Published IEEE 01.08.1999
Subjects
Deformation
Equations
Face detection
Formability
Handicapped aids
Handles
Image segmentation
Image sequences
Intelligence
Maximum likelihood detection
Nearest neighbor searches
Pattern analysis
Segmentation
Segments
Shape
System testing
Vectors
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Summary:We present a prediction-and-verification segmentation scheme using attention images from multiple fixations. A major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. The scheme is also relatively efficient. The system was tested to segment hands in sequences of intensity images, where each sequence represents a hand sign in American Sign Language. The experimental result showed a 95 percent correct segmentation rate with a 3 percent false rejection rate.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.784311