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Shape Correspondence for Statistical Shape Modeling: Algorithms and Performance Evaluation.(English)

Shape Correspondence for Statistical Shape Modeling: Algorithms and Performance Evaluation.(English)

          
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About the Book

In order to accurately measure structural shape and its possible variation, statistical shape analysis has become a major research topic in computer vision and medical image analysis in recent years. In statistical shape analysis a population of shape instances is given where each shape instance is in the form of a smooth 2D contour or a smooth 3D surface. The goal is to construct a statistical shape model that accurately captures the variability of the given shape structure described by the population of shape instances. In constructing a statistical shape model the first step is to identify a set of landmarks for each shape instance, where a landmark is defined as a point of correspondence across the population that can be used to examine and measure shape change. In general these landmarks can be identified manually by a human (expert), or automatically via software. Manually identifying corresponded landmarks can be achieved, however such a method is both subjective and error prone. Because of this, developing more accurate and efficient shape correspondence methods that automate the landmark identification process has been widely investigated over the last several years. Even though much progress has been made, the development of an efficient and accurate shape correspondence method that scales favorably to the size of the population is still a largely unsolved problem. Another open problem in statistical shape analysis is the objective evaluation of these shape correspondence methods. One major reason is the unavailability of a ground-truth shape correspondence, which would be defined by a group of experts that manually identify the corresponded landmarks. Currently, this limitation is addressed by three general measures that are used to evaluate the shape correspondence performance. These three measures describe the properties of the statistical shape model constructed from a shape correspondence result and not against some known ground-truth. The research presented in this dissertation attempts to address these two problems by developing an efficient and accurate shape correspondence method that scales well to the size of the population, and develop a shape correspondence benchmark to objectively evaluate shape correspondence performance against some known ground-truth.


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Product Details
  • ISBN-13: 9781244040533
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 254 mm
  • No of Pages: 110
  • Series Title: English
  • Sub Title: Algorithms and Performance Evaluation.
  • Width: 203 mm
  • ISBN-10: 1244040533
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 7 mm
  • Weight: 236 gr


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