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Two dimensional convolute integers for machine vision and image recognitionMachine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.
Document ID
19880019985
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Edwards, Thomas R.
(TREC, Inc. Huntsville, AL, United States)
Date Acquired
September 5, 2013
Publication Date
August 1, 1988
Publication Information
Publication: NASA, Marshall Space Flight Center, Second Conference on Artificial Intelligence for Space Applications
Subject Category
Cybernetics
Accession Number
88N29369
Distribution Limits
Public
Copyright
Public Use Permitted.
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