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Parameter Estimation in Image Processing and Computer Vision

  • Christoph S. Garbe
  • Björn Ommer
Chapter
Part of the Contributions in Mathematical and Computational Sciences book series (CMCS, volume 4)

Abstract

Parameter estimation plays a dominant role in a wide number of image processing and computer vision tasks. In these settings, parameterizations can be as diverse as the application areas. Examples of such parameters are the entries of filter kernels optimized for a certain criterion, image features such as the velocity field, or part descriptors or compositions thereof. Subsequently, approaches for estimating these parameters encompass a wide range of techniques, often tuned to the application, the underlying data and viable assumptions. Here, an overview of parameter estimation in image processing and computer vision will be given. Due to the wide and diverse areas in which parameter estimation is applicable, this review does not claim completeness. Based on selected key topics in image processing and computer vision we will discuss parameter estimation, its relevance, and give an overview over the techniques involved.

Keywords

Optical Flow Training Image Confidence Measure Motion Field Image Inpainting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Interdisciplinary Center for Scientific Computing (IWR)University of HeidelbergHeidelbergGermany

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