Apparatus for generating a feature matrix based on normalized out-class and in-class variation matrices
First Claim
1. A device for acquiring images, extracting image information, storing the image information, and comparing the image information with query image information comprising:
- first means for acquiring images;
second means for processing said images into image information;
third means for determining the most distinctive aspects of said image information;
said third means including means for performing an In-Class to Out-of-Class study;
fourth means for forming and storing database feature vectors comprising the magnitudes of said most distinctive aspects of said image information; and
fifth means for querying said fourth means to determine whether a query feature vector is sufficiently similar to any of said database feature vectors,wherein said means for performing an In-Class to Out-of-Class study includes;
means for generating an In-Class Variation Matrix;
means for generating an Out-Class Variation Matrix;
means for normalizing said In-Class Variation Matrix to form a normalized In-Class Variation Matrix;
means for normalizing said Out-Class Variation Matrix to form a normalized Out-Class Variation Matrix;
means for generating a feature matrix, each component of said feature matrix corresponding to a ratio between corresponding components of said normalized Out-Class Variation Matrix and said normalized In-Class Variation Matrix;
means for normalizing said feature matrix into a Normalized Feature Matrix;
means for partitioning said Normalized Feature Matrix into bricks;
means for prioritizing said bricks; and
means for creating a feature template vector whose elements correspond to a subset of said bricks.
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Abstract
A method and apparatus under software control for pattern recognition utilizes a neural network implementation to recognize two dimensional input images which are sufficiently similar to a database of previously stored two dimensional images. Images are first image processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the highest order (most discriminatory) magnitude information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network will result in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate that a successful identification has been made. Where a successful identification has occurred, the unique identifier number may be displayed.
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Citations
10 Claims
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1. A device for acquiring images, extracting image information, storing the image information, and comparing the image information with query image information comprising:
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first means for acquiring images; second means for processing said images into image information; third means for determining the most distinctive aspects of said image information; said third means including means for performing an In-Class to Out-of-Class study; fourth means for forming and storing database feature vectors comprising the magnitudes of said most distinctive aspects of said image information; and fifth means for querying said fourth means to determine whether a query feature vector is sufficiently similar to any of said database feature vectors, wherein said means for performing an In-Class to Out-of-Class study includes; means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix to form a normalized In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix to form a normalized Out-Class Variation Matrix; means for generating a feature matrix, each component of said feature matrix corresponding to a ratio between corresponding components of said normalized Out-Class Variation Matrix and said normalized In-Class Variation Matrix; means for normalizing said feature matrix into a Normalized Feature Matrix; means for partitioning said Normalized Feature Matrix into bricks; means for prioritizing said bricks; and means for creating a feature template vector whose elements correspond to a subset of said bricks. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification