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The Radon Transform and Some of Its Applications

The Radon Transform and Some of Its Applications by Stanley R. Deans

The Radon Transform and Some of Its Applications



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The Radon Transform and Some of Its Applications Stanley R. Deans ebook
ISBN: 9780486462417
Publisher: Dover Publications
Page: 304
Format: pdf


Written by one of the chief contributors to the modern theory of the Radon transform, this book gives a systematic and concise exposition on the field. The Fourier Transform and its Applications Video Lectures, Stanford Online Course, free tutorials and lecture notes, free download, Educational Lecture Videos. They propose a similar method for Radon in the sense that it also calculates functionals of the image function along lines criss-crossing its domain. Key Words: Electron microscopy - image processing - three-dimensional reconstruction - Radon transform - image alignment - image classification - cross-correlation - single particles - random conical - angular refinement. The most significant changes in the second edition include: New chapters on fractional calculus and its applications to ordinary and partial differential equations, wavelets and wavelet transformations, and Radon transform. Applications, exercises, comments, and observations with some sections entirely rewritten. Radon Transform Hough Transform is the derivative of the Radon Transform. Physical Law that the infinite series of terms in a perturbative expansion makes the idea that nature is mathematical in nature crazy. It contains more than 611 worked examples and exercises with answers as well as hints to selected exercises. The second part deals with the Fourier transform and its applications to classical partial differential equations and the Radon transform; a clear introduction to the subject serves to avoid technical difficulties. This volume covers classical linear systems theory and its applications to biomedicine. It also describes the analytical signal and the Hilbert transform and some of its biomedical applications. It examines the important use of joint time-frequency analysis to characterize non-stationary physiological signals, and explores the mathematics of tomographic imaging (the Radon transform, the Fourier slice theorem, and the filtered back-projection algorithm).

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