On designing an isotropic fiducial mark

The author has at last been able to fulfil his intention of extending to the determination of the torsion constants of crystals (and any other small bodies, unprocurable in large pieces) the accuracy and refinement of the measurement by wave-lengths of light. It is comparable to the method of determining Young’s modulus of elasticity by the bending of plates and bars, by the use of the interferential “elasmometer,” which the author described to the Royal Society in 1903. The torsometer now described, used with the universal interferometer described in the preceding memoir, does for torsion what the elasmometer does for bending, so that it is now possible to determine the whole of the elastic constants of crystals, which in the cases of crystals with minimum symmetry (triclinic) are no less than 21 in number, by the refined method of the half-wave-length scale of parallel and rectilinear interference bands, and their micrometrically determined fractions. The torsometer itself is a refinement of the instrument devised and used by the late Prof. W. Voigt, in which the method of the optical lever was employed. The new torsometer transmits the torse movement instead to a Grayson-ruling signal, or fiducial mark, consisting of five engraved lines only one forty-thousandth of an inch apart, the movement of the central line of which is followed by the microscope and measured in interference bands, the torsometer being fixed on the work-table of the universal interferometer for the purpose. This is, indeed, the first practical application of the new interferometer.

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Research on small fiducial mark use for robotic manipulation and alignment of ophthalmic lenses

1999 7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA '99 (Cat. No.99TH8467) ◽

10.1109/etfa.1999.813117 ◽

2003 ◽

Cited By ~ 5

Author(s):

X. Fernandez ◽

J. Amat

Keyword(s):

Robotic Manipulation ◽

Fiducial Mark

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Automatic Extraction Of Interior Orientation Data In Aerial Photography Using Image Matching Method

Journal of Applied Geospatial Information ◽

10.30871/jagi.v5i2.2115 ◽

2021 ◽

Vol 5 (2) ◽

pp. 510-514

Author(s):

Helmy Mukti Wijaya ◽

Teguh Hariyanto ◽

Hepi Hapsari Handayani

Keyword(s):

Image Matching ◽

Cross Correlation ◽

Focal Length ◽

Least Square ◽

Programming Algorithm ◽

Matching Method ◽

Orientation Data ◽

Fiducial Mark ◽

Normalized Cross Correlation ◽

Matching Techniques

The Interior Orientation is a set of parameters that have been determined to transform the coordinates of the camera photo, that is the coordinates of the pixel leading to the coordinates of the image. This parameter is used to calibrate the camera before use so as to produce a precise measurement from an aerial photograph. This orientation parameter consists of a calibrated and equivalent camera focal length, lens distortion, principal point, fiducial mark location, camera resolution, and flatness of the focal plane. All of these parameters are attached to or contained on the camera sensor and the values of these parameters can usually be known from the camera's report page. In this work, the author wants to obtain pixel coordinates from the Fiducial Mark in the base image (Window Search) automatically, therefore a Fiducial Mark template was created which is formed from a piece of a photo image frame to determine the Fiducial Mark coordinate values from the base image ( Window Search), the basis of this programming is to use the concept of photogrammetry, which uses Image Matching techniques. The Image Matching process was developed from the C ++ Language programming algorithm platform, this was done in order to speed up computational results. There are a number of techniques for doing Image Matching, in this study the authors conducted using the Normalized Cross-Correlation Image Matching. In statistics Normalized Cross-Correlation is between two random variables by determining the size of how closely the two variables are different simultaneously. Similarly, Normalized Cross-Correlation in Image Matching is a measurement by calculating the degree of similarity between two images. This level of similarity is determined by Normalized Cross-Correlation (NCC). The Least Square Image Matching method is used to increase the accuracy of the coordinates of the conjugation points.

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