Correlation Pattern Recognition


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Abstract: The traditional approach to understanding the information encoded in spectroscopic data has been first to assign each transition observed and then to relate the positions and intensities of the transitions to a model that allows insight into the system being studied. In complex and congested spectra, however, the process of assignment may be difficult, tedious, or even impossible. The XAC can be used to locate patterns that are parameterized in a complex way within a congested spectrum.

Correlation Pattern Recognition in Nonoverlapping Scene Using a Noisy Reference

The XCC recognizes patterns that are common among two or more spectra. Tests of these techniques using synthetic spectra will be presented, as well as application to the mass spectra of large molecules XAC and FTIR spectra of mixtures of ammonia isotopemers XCC.


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    Correlation pattern recognition for biometrics

    Not Accessible Your account may give you access. Abstract This paper describes some previously invented optical means for performing convolutions i.

    Ill-posed problem of the constant-constraint criterion in optical correlation pattern recognition Tao ChunKan J. Normalized correlation for pattern recognition Fred M. Optical pattern recognition by use of a segmented semiconductor optical amplifier P. More Recommended Articles. One-dimensional logarithmic harmonic synthetic discriminant function filters for shift-, scale-, and projection-invariant pattern recognition Jianping Yao and Guy Lebreton Opt.

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    Correlation Pattern Recognition in Nonoverlapping Scene Using a Noisy Reference

    Microcomputer-based programmable optical correlator for automatic pattern recognition and identification Francis T. References You do not have subscription access to this journal. Cited By You do not have subscription access to this journal.

    Cognition 2.2 - Pattern Recognition

    Figures 11 You do not have subscription access to this journal. Equations 12 You do not have subscription access to this journal. Journal of the Optical Society of America.

    Please login to set citation alerts. Equations displayed with MathJax. Rotation, dilation and distortion of fluid flow within the interrogation region and intensity changes of particles introduced correlation errors or bias.

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    As a consequence, the size of the interrogation area and the time interval between images must be kept small to avoid these problems. A feature-recognition method is proposed here for analyzing PIV images. It first extracts structural features of the particle pattern after their locations have been isolated from images. A preliminary process is to replace the particle images by the Cartesian coordinates of particle centers.

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    In this way the brightness of particle images plays no further part, and the point positions are used to establish structural features: topological relations between each point and its neighbors. The interrogation area is defined by a limited number of neighboring points. The size and shape of each interrogation area varies with the distribution of neighbors. A fit to motion, rotation and distortion among the neighbors is then carried out in the space of topological relations of successive images.

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    Correlation Pattern Recognition Correlation Pattern Recognition
    Correlation Pattern Recognition Correlation Pattern Recognition
    Correlation Pattern Recognition Correlation Pattern Recognition
    Correlation Pattern Recognition Correlation Pattern Recognition
    Correlation Pattern Recognition Correlation Pattern Recognition

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