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Computer Vision Analysis of Image Motion by Variational Methods (Springer Topics in Signal Processing) Hardcover – September 18, 2013

ISBN-13: 978-3319007106 ISBN-10: 3319007106 Edition: 2014th

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Product Details

  • Series: Springer Topics in Signal Processing (Book 10)
  • Hardcover: 207 pages
  • Publisher: Springer; 2014 edition (September 18, 2013)
  • Language: English
  • ISBN-10: 3319007106
  • ISBN-13: 978-3319007106
  • Product Dimensions: 9.2 x 6.1 x 0.7 inches
  • Shipping Weight: 15.2 ounces (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #6,421,503 in Books (See Top 100 in Books)

Editorial Reviews

From the Back Cover

This book presents a unified view of image motion analysis under the variational framework. Variational methods, rooted in physics and mechanics, but appearing in many other domains, such as statistics, control, and computer vision, address a problem from an optimization standpoint, i.e., they formulate it as the optimization of an objective function or functional. The methods of image motion analysis described in this book use the calculus of variations to minimize (or maximize) an objective functional which transcribes all of the constraints that characterize the desired motion variables. The book addresses the four core subjects of motion analysis: Motion estimation, detection, tracking, and three-dimensional interpretation. Each topic is covered in a dedicated chapter. The presentation is prefaced by an introductory chapter which discusses the purpose of motion analysis. Further, a chapter is included which gives the basic tools and formulae related to curvature, Euler Lagrange equations, unconstrained descent optimization, and level sets, that the variational image motion processing methods use repeatedly in the book.

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0 of 1 people found the following review helpful By Eugene Tsiang on November 26, 2013
Format: Kindle Edition Verified Purchase
What's missing is the establishment of a clear relationship between the Fundamental Matrix and the Longuet-Higgins/Prozdny equations, the latter being an infinitesimal version of the first.
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