Image analysis – Pattern recognition – Template matching
Reexamination Certificate
1998-04-03
2001-09-25
Au, Amelia M. (Department: 2623)
Image analysis
Pattern recognition
Template matching
C382S260000, C382S279000
Reexamination Certificate
active
06295373
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to pattern recognition, and more particularly, to correlation filters used in pattern recognition.
2. Description of Related Art
Two-dimensional correlation techniques have used spatial filters (known as correlation filters) to detect, locate and classify targets in observed scenes. A correlation filter should attempt to yield: sharp correlation peaks for targets of interest, high discrimination against unwanted objects, excellent robustness to noise in the input scene and high tolerance to distortions in the input. A variety of filters to address these aspects and other aspects have been proposed (for example, see: B. V. K. Vijaya Kumar, “Tutorial Survey of Composite Filter Designs for Optical Correlators,”
Applied Optics
, Vol. 31, pp. 4773-4801, 1992).
Linear filters known as Synthetic Discriminant Function (SDF) filters have been introduced by Hester and Casasent as well as by Caulfield and Maloney (see: C. F. Hester and D. Casasent, “Multivariant Techniques for Multiclass Pattern Recognition,”
Applied Optics
, Vol. 19, pp. 1758-1761, 1980; H. J. Caulfield and W. T. Maloney, “Improved Discrimination in Optical Character Recognition,”
Applied Optics
, Vol. 8, pp. 2354-2356, 1969).
Other correlation filters include the minimum squared error Synthetic Discriminant Function (MSE SDF) where the correlation filter is selected that yields the smallest average squared error between the resulting correlation outputs and a specified shape (see: B. V. K. Vijaya Kumar, A. Mahalanobis, S. Song, S. R. F. Sims and J. Epperson, “Minimum Squared Error Synthetic Discriminant Functions,”
Optical Engineering
, Vol. 31, pp. 915-922, 1992).
Another filter is the maximum average correlation height (MACH) filter that determines and uses the correlation shape yielding the smallest squared error (see: A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, J. Epperson, “Unconstrained Correlation Filters,”
Applied Optics
, Vol. 33, pp. 3751-3759, 1994). However, the MACH filter and other current filters generally perform only linear operations on input image data and consequently are limited in their performance to detect patterns within the input image data. Moreover, the current approaches suffer the disadvantage of an inadequate ability to process information from multiple sensors as well as at different resolution levels.
SUMMARY OF THE INVENTION
The present invention is a method and apparatus for detecting a pattern within an image. Image data is received which is representative of the image. Filter values are determined which substantially optimize a first predetermined criterion. The first predetermined criterion is based upon the image data. A correlation output is generated using a non-linear polynomial relationship based upon the determined filter values and the image data. The correlation output is indicative of the presence of the pattern within the image data.
The present invention contains the following features (but is not limited to): improved probability of correct target recognition, clutter tolerance and reduced false alarm rates. The present invention also contains such features as (but is not limited to): detection and recognition of targets with fusion of data from multiple sensors, and the ability to combine optimum correlation filters with multi-resolution information (such as Wavelets and morphological image transforms) for enhanced performance.
Additional advantages and features of the present invention will become apparent from the subsequent description and the appended claims, taken in conjunction with the accompanying drawings in which:
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Kumar B. V. K. Vijaya
Mahalanobis Abhijit
Alkov Leonard A.
Au Amelia M.
Dastouri Mehrdad
Lenzen, Jr. Glenn H.
Raytheon Company
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