Image analysis – Pattern recognition – Classification
Reexamination Certificate
2006-03-07
2006-03-07
Zimmerman, Mark (Department: 2625)
Image analysis
Pattern recognition
Classification
C382S165000, C382S190000, C382S224000, C382S276000, C345S644000, C375S240160
Reexamination Certificate
active
07010167
ABSTRACT:
Feature attributes are extracted from an observation space to create feature vectors for each class to be identified. A linear transformation matrix is used to reduce the dimension of the feature vectors. A numerical optimization algorithm maximizes a geometric criterion function in order to calculate the linear transformation matrix, where it exploits the geometry of the class contours of constant density. Next, a classifier based on the feature vectors in a lower dimension is generated and a class is determined for the data represented.
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Meyer Gerard G. L.
Ordowski Mark Lawrence
Bloor Stephen M.
Liew Alex
The United States of America as represented by the National Secu
Zimmerman Mark
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