Orthogonal Linear Regression in Roentgen Stereophotogrammetry

Patrick Atkinson; Jesse V. Benny; Brian J. McCartin

Orthogonal Linear Regression in Roentgen Stereophotogrammetry

Patrick Atkinson, Jesse V. Benny, Brian J. McCartin

Kettering University

Research output: Contribution to journal › Article › peer-review

Abstract

Rooted in aerial reconnaissance, mathematical photogrammetry has evolved into a mainstay of biomedical image processing. The present paper develops an algorithm for Roentgen stereophotogrammetry, a method of imaging musculoskeletal systems both static and dynamic, which incorporates a number of novel features employing techniques from projective geometry, orthogonal regression and least squares approximation. Theoretical and numerical evidence is presented of the efficacy of the proposed procedure.

Original language	American English
Journal	Applied Mathematical Sciences
Volume	1
State	Published - Jan 1 2007

Keywords

Mathematical Photogrammetry; Projective Geometry; Orthogonal Regression; Least Squares Approximation

Disciplines

Bioimaging and Biomedical Optics
Biomedical Devices and Instrumentation
Biomedical Engineering and Bioengineering
Engineering

Access to Document

Orthogonal Linear Regression in Roentgen StereophotogrammetryFinal published version, 697 KBLicense: CC BY

Cite this

@article{f76fce98a6e14bc39c707663c7c7de74,

title = "Orthogonal Linear Regression in Roentgen Stereophotogrammetry",

abstract = " Rooted in aerial reconnaissance, mathematical photogrammetry has evolved into a mainstay of biomedical image processing. The present paper develops an algorithm for Roentgen stereophotogrammetry, a method of imaging musculoskeletal systems both static and dynamic, which incorporates a number of novel features employing techniques from projective geometry, orthogonal regression and least squares approximation. Theoretical and numerical evidence is presented of the efficacy of the proposed procedure.",

keywords = "Mathematical Photogrammetry; Projective Geometry; Orthogonal Regression; Least Squares Approximation",

author = "Patrick Atkinson and Benny, {Jesse V.} and McCartin, {Brian J.}",

year = "2007",

month = jan,

day = "1",

language = "American English",

volume = "1",

journal = "Applied Mathematical Sciences",

}

TY - JOUR

T1 - Orthogonal Linear Regression in Roentgen Stereophotogrammetry

AU - Atkinson, Patrick

AU - Benny, Jesse V.

AU - McCartin, Brian J.

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Rooted in aerial reconnaissance, mathematical photogrammetry has evolved into a mainstay of biomedical image processing. The present paper develops an algorithm for Roentgen stereophotogrammetry, a method of imaging musculoskeletal systems both static and dynamic, which incorporates a number of novel features employing techniques from projective geometry, orthogonal regression and least squares approximation. Theoretical and numerical evidence is presented of the efficacy of the proposed procedure.

AB - Rooted in aerial reconnaissance, mathematical photogrammetry has evolved into a mainstay of biomedical image processing. The present paper develops an algorithm for Roentgen stereophotogrammetry, a method of imaging musculoskeletal systems both static and dynamic, which incorporates a number of novel features employing techniques from projective geometry, orthogonal regression and least squares approximation. Theoretical and numerical evidence is presented of the efficacy of the proposed procedure.

KW - Mathematical Photogrammetry; Projective Geometry; Orthogonal Regression; Least Squares Approximation

UR - https://digitalcommons.kettering.edu/mech_eng_facultypubs/65

M3 - Article

VL - 1

JO - Applied Mathematical Sciences

JF - Applied Mathematical Sciences

ER -

Orthogonal Linear Regression in Roentgen Stereophotogrammetry

Abstract

Keywords

Disciplines

Access to Document

Other files and links

Cite this