Precision Landmark Location for Machine Vision and Photogrammetry [electronic resource] : Finding and Achieving the Maximum Possible Accuracy /
Gutierrez, José A.author.
Armstrong, Brian S.R.author.
The applications of image-based measurement are many and various: image-guided surgery, mobile-robot navigation, component alignment, part inspection and photogrammetry, among others. In all these applications, landmarks are detected and located in images, and measurements made from those locations. Precision Landmark Location for Machine Vision and Photogrammetry addresses the ubiquitous problem of measurement error associated with determining the location of landmarks in images. With a detailed model of the image formation process and landmark location estimation, the Cramér-Rao Lower Bound (CRLB) theory of statistics is applied to determine the least possible measurement uncertainty in a given situation. This monograph provides the reader with: • the most complete treatment to date of precision landmark location and the engineering aspects of image capture and processing; • detailed theoretical treatment of the CRLB; • a software tool for analyzing the potential performance-specific camera/lens/algorithm configurations; • two novel algorithms which achieve precision very close to the CRLB; • an experimental method for determining the accuracy of landmark location; • downloadable MATLAB® package to assist the reader with applying theoretically-derived results to practical engineering configurations. All of this adds up to a treatment that is at once theoretically sound and eminently practical. Precision Landmark Location for Machine Vision and Photogrammetry will be of great interest to computer scientists and engineers working with and/or studying image processing and measurement. It includes cutting-edge theoretical developments and practical tools so it will appeal to research investigators and system designers.
Description based upon print version of record.
Includes bibliographical references and index.
Physics of Digital Image Formation -- Analytic Framework for Landmark Location Uncertainty -- Model-based Landmark Location Estimators -- Two-dimensional Noncollocated Numerical Integration -- Computational Tools -- Experimental Validation -- Studies of Landmark Location Uncertainty -- Conclusions.
The applications of image-based measurement are many and various: image-guided surgery, mobile-robot navigation, component alignment, part inspection and photogrammetry, among others. In all these applications, landmarks are detected and located in images, and measurements made from those locations. Precision Landmark Location for Machine Vision and Photogrammetry addresses the ubiquitous problem of measurement error associated with determining the location of landmarks in images. With a detailed model of the image formation process and landmark location estimation, the Cramér-Rao Lower Bound (CRLB) theory of statistics is applied to determine the least possible measurement uncertainty in a given situation. This monograph provides the reader with: • the most complete treatment to date of precision landmark location and the engineering aspects of image capture and processing; • detailed theoretical treatment of the CRLB; • a software tool for analyzing the potential performance-specific camera/lens/algorithm configurations; • two novel algorithms which achieve precision very close to the CRLB; • an experimental method for determining the accuracy of landmark location; • downloadable MATLAB® package to assist the reader with applying theoretically-derived results to practical engineering configurations. All of this adds up to a treatment that is at once theoretically sound and eminently practical. Precision Landmark Location for Machine Vision and Photogrammetry will be of great interest to computer scientists and engineers working with and/or studying image processing and measurement. It includes cutting-edge theoretical developments and practical tools so it will appeal to research investigators and system designers.
Optical data processing.
Control engineering.
Robotics.
Mechatronics.
Remote sensing.
Signal processing.
Image processing.
Speech processing systems.
Radiology.
Statistics .
Image Processing and Computer Vision.
Control, Robotics, Mechatronics.
Remote Sensing/Photogrammetry.
Signal, Image and Speech Processing.
Imaging / Radiology.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.