FAW for multi-exposure fusion features

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationAdvances in Image and Video Technology
Subtitle of host publication5th Pacific Rim Symposium, PSIVT 2011, Gwangju, South Korea, November 20-23, 2011, Proceedings, Part I
EditorsYo-Sung Ho
Place of PublicationHeidelberg, Germany
PublisherSpringer Nature
Number of pages12
ISBN (Electronic)9783642253683
ISBN (Print)9783642253669
Publication statusPublished - 7 Nov 2011

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • feature fusion
  • SIFT
  • HDR
  • LDR
  • tone mapping
  • exposure fusion
  • stero


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