2009
DOI: 10.1007/978-0-387-78747-3
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Image Correlation for Shape, Motion and Deformation Measurements

Abstract: except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

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Cited by 729 publications
(612 citation statements)
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“…The nearest neighbor distance (NND) was calculated by the shortest distance from the center of gravity of each pit to another pits center of gravity. It is necessary to obtain at least 3×3 pixels patterns to allow the use of DIC [3,4,16]; hence, only pits with a minimum area of 2.69 μm 2 were selected (area of 3×3 pixels in 63X magnification).…”
Section: Pit Etchingmentioning
confidence: 99%
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“…The nearest neighbor distance (NND) was calculated by the shortest distance from the center of gravity of each pit to another pits center of gravity. It is necessary to obtain at least 3×3 pixels patterns to allow the use of DIC [3,4,16]; hence, only pits with a minimum area of 2.69 μm 2 were selected (area of 3×3 pixels in 63X magnification).…”
Section: Pit Etchingmentioning
confidence: 99%
“…The pattern also has to show a high speckle density and good contrast to limit errors. It must also be of suitable size, which means it must not be too big, cover an entire grain (for micro-scale level analysis), nor smaller than 3×3 pixels to allow matching in the full field of view [4].…”
Section: Introductionmentioning
confidence: 99%
“…Note that [R] allows any 3D rotation, and can therefore compensate for any misalignment of the image verticals with the mirror plane. The calibration process used is a simplified form of the bundled adjustment procedure as described by Sutton et al [11], solving for just the rotation and translation between camera systems. Firstly, using interactive routines from Matlab's image processing toolbox [8], the user manually selects a number of corresponding pairs of points from the true and reflected image, as shown in Figure 2.…”
Section: Calibrationmentioning
confidence: 99%
“…where n 1 , n 2 and n 3 are components of a vector forming the minimal parametrisation of [R], where the direction of the vector defines the axis of rotation, and the magnitude of its angle in radians [11], and t 1 , t 2 and t 3 are components of the translation vector {t}. Hence to calculatex n (β ), the procedure calculates the 3D position of the point P n implied by the true and reflected points and current parameters, using the method described in Section 3.5, and then evaluates:…”
Section: Calibrationmentioning
confidence: 99%
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