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001 978-1-84628-913-2
003 DE-He213
005 20161121230715.0
007 cr nn 008mamaa
008 100301s2008 xxk| s |||| 0|eng d
020 _a9781846289132
_9978-1-84628-913-2
024 7 _a10.1007/978-1-84628-913-2
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aArmstrong, Brian S.R.
_eauthor.
245 1 0 _aPrecision Landmark Location for Machine Vision and Photogrammetry
_h[electronic resource] :
_bFinding and Achieving the Maximum Possible Accuracy /
_cby Brian S.R. Armstrong, José A. Gutierrez.
264 1 _aLondon :
_bSpringer London,
_c2008.
300 _aXI, 162 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPhysics 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.
520 _aThe 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.
650 0 _aComputer science.
650 0 _aRadiology.
650 0 _aImage processing.
650 0 _aRemote sensing.
650 0 _aStatistics.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aRemote Sensing/Photogrammetry.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aImaging / Radiology.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700 1 _aGutierrez, José A.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781846289125
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84628-913-2
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c502770
_d502770