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FAW for Multi-exposure Fusion Features

  • Michael May
  • Martin Turner
  • Tim Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

This paper introduces a process where fusion features assist matching scale invariant feature transform (SIFT) image features from high contrast scenes. FAW defines the order for extracting features: features, alignment then weighting. The process uses three quality measures to select features from a series of differently exposed images and select a subset of the features in favour of those areas that are defined as well exposed from the different images. The results show an advantage in using these features over features extracted from the common alternative techniques of exposure fusion and tone mapping which extract the features as AWF; alignment, weighting then features. This paper also shows that the process allows for a more robust response when using misaligned or stereoscopic image sets.

Keywords

feature fusion SIFT HDR LDR tone mapping exposure fusion stereo 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael May
    • 1
  • Martin Turner
    • 1
  • Tim Morris
    • 1
  1. 1.The University of ManchesterUK

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