Magneto-Optic Image Classification Using Neural Networks

Nath, S.; Fulton, J. P.; Wincheski, Buzz

doi:10.1007/978-1-4613-0383-1_116

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Compared to a classical Eddy-Current method, the gain of time is about 8. Nevertheless, interpretation requires qualified people even when a lot of methods are proposed for helping the detection of flaws by a complementary image processing and for improving the MOI® capabilities in order to detect corrosion or cracks close to fasteners [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

3D Modeling of a Magneto-Optic Imager by a Dyadic Green’s Functions Approach

Decitre¹,

Prémel²,

Lemistre³

2003

AIP Conference Proceedings

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A 3D semi-analytical model based on dyadic Green's functions is proposed to solve the forward problem for a magneto-optic imager with a simplified eddy-current excitation. The normal component of the magnetic field is evaluated in place of the ferrimagnetic garnet. A comparison between data provided by the model and data computed by a 3D finite element method is presented. Theoretical results are also validated with experimental data obtained by a Magneto-Optic Imager designed by Physical Research Incorporation.

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Section: Introductionmentioning

confidence: 99%

3D Modeling of a Magneto-Optic Imager by a Dyadic Green’s Functions Approach

Decitre¹,

Prémel²,

Lemistre³

2003

AIP Conference Proceedings

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show abstract

“…The preprocessed data are then combined in the data fusion module using an appropriate strategy [15,16,18,[30][31][32]. The fused data can be analyzed and evaluated further to extract relevant information using appropriate classification and characterization algorithms [33][34][35][36][37][38][39].…”

Section: Data Fusion In Ndementioning

confidence: 99%

NDE data fusion using morphological approaches

Song¹

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This manuscript has been reproduced from the microfihn master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter &ce, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. £Bgher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI A Bell & Howell Infonnation Company 300 North Zed> Road, Ann Arbor MI 48106-1346 USA 313/761-4700 800/521-0600 NDE data fusion using morphological approaches by Young-Won Song A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of

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<title>Invariance algorithms for processing NDE signals</title>

Mandayam

Удпа

et al. 1996

SPIE Proceedings

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Magneto-Optic Image Classification Using Neural Networks

Cited by 3 publications

References 4 publications

3D Modeling of a Magneto-Optic Imager by a Dyadic Green’s Functions Approach

3D Modeling of a Magneto-Optic Imager by a Dyadic Green’s Functions Approach

NDE data fusion using morphological approaches

<title>Invariance algorithms for processing NDE signals</title>

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