It seems like the summer of dreams come true for Hollywood princess Kaitlin Burke: the media loves her (again), super-cute and funny Austin Meyers is finally her boyfriend and she's starring in a film by her all-time favourite director Hutch Adams.
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 Cramer--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(R) 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.
Although modern location theory is now more than 90 years old, the focus of researchers in this area has been mainly problem oriented. However, a common theory, which keeps the essential characteristics of classical location models, is still missing.
This monograph addresses this issue. A flexible location problem called the Ordered Median Problem (OMP) is introduced. For all three main subareas of location theory (continuous, network and discrete location) structural properties of the OMP are presented and solution approaches provided. Numerous illustrations and examples help the reader to become familiar with this new location model.
By using OMP classical results of location theory can be reproved in a more general and sometimes even simpler way. Algorithms enable the reader to solve very flexible location models with a single implementation. In addition, the code of some algorithms is available for download.
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