3-D modelling and classification in automated target recognition

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dc.degree.department Electrical and Computer Engineering en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.degree.grantor Texas Tech University en_US
dc.degree.level Doctoral en_US
dc.degree.name Ph .D . en_US
dc.rights.availability unrestricted en_US
dc.creator Nutter , Brian Steven en_US
dc.date.accessioned 2014 -02 -19T18 :49 :01Z
dc.date.available 2011 -02 -19T00 :28 :28Z en_US
dc.date.available 2014 -02 -19T18 :49 :01Z
dc.date.issued 1990 -08 en_US
dc.identifier.uri http : / /hdl .handle .net /2346 /21652 en_US
dc.description.abstract Automated target recognition (ATR ) using a computer vision system is a problem of extremely high complexity . A 3 -D object recognition scheme involves many image analysis and enhancement techniques , including image processing , image segmentation , image registration , and object modeling and projection . This dissertation addresses the problem of 3 -D object recognition using five distinct methods of matching image data with model projection -derived data . In analyzing each digitized video image , a variety of techniques , including an optimal gray level map for correlating binary line drawings with gradient images , were used to enhance the visibility of particular features and to increase signal to noise ratios in images . The shapes extracted from these enhanced images were then analyzed in a number of fashions , including the statistical descriptors of Karhunen -Loeve transformation . The first of the five object identification methods which were tested compared descriptors of the object to be analyzed with those of model projections meeting certain criteria . The second compared the object descriptors to those of a precalculated series of model projections . The third method used the descriptions of the second method as a starting point for a neural network , and then proceeded to learn the differences between these model projections and actual data . The neural net as realized demonstrated a great reduction in training time over conventional implementations . Calibration difficulties of other methods were greatly reduced by the learning capability of the neural net . The fourth method cross -correlated the optimally mapped gradient of the object image with a series of model projections . Finally , ways of combining these methods to utilize the strengths of each were investigated . Superior accuracy was obtained for cross -correlation . Optimal techniques which significantly reduced the number of required correlations and hence the computational load were also found to give very accurate results . en_US
dc.language.iso en _US en_US
dc.publisher Texas Tech University en_US
dc.subject Image processing - - Digital techniques en_US
dc.subject Computer vision en_US
dc.title 3 -D modelling and classification in automated target recognition en_US
dc.type Electronic Dissertation en_US

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3-D modelling and classification in automated target recognition. Doctoral dissertation, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /21652 .

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