Multi-exposure Fusion Features

This paper introduces a process where fusion features assist matching scale invariant feature transform (SIFT) image features with high contrast scenes where areas would be under or over exposed in a single image. The process uses three quality measures to select features from a series of differently exposed images and weight the featuresin favour of those areas that are well exposed. The results show an advantage in using these features over features extracted from the common alternative techniques of exposure fusion and tone mapping. This paper also shows that the process allows for a more robust response when using misaligned or stereoscopic image sets.