CP HDR: A feature point detection and description library for LDR and
  HDR images
Artur Santos Nascimento Valter Guilherme Silva de Souza Daniel Oliveira Dantas Beatriz Trinchão Andrade
In computer vision, characteristics refer to image regions with unique
properties, such as corners, edges, textures, or areas with high contrast.
These regions can be represented through feature points (FPs). FP detection and
description are fundamental steps to many computer vision tasks. Most FP
detection and description methods use low dynamic range (LDR) images,
sufficient for most applications involving digital images. However, LDR images
may have saturated pixels in scenes with extreme light conditions, which
degrade FP detection. On the other hand, high dynamic range (HDR) images
usually present a greater dynamic range but FP detection algorithms do not take
advantage of all the information in such images. In this study, we present a
systematic review of image detection and description algorithms that use HDR
images as input. We developed a library called CP_HDR that implements the
Harris corner detector, SIFT detector and descriptor, and two modifications of
those algorithms specialized in HDR images, called SIFT for HDR (SfHDR) and
Harris for HDR (HfHDR). Previous studies investigated the use of HDR images in
FP detection, but we did not find studies investigating the use of HDR images
in FP description. Using uniformity, repeatability rate, mean average
precision, and matching rate metrics, we compared the performance of the CP_HDR
algorithms using LDR and HDR images. We observed an increase in the uniformity
of the distribution of FPs among the high-light, mid-light, and low-light areas
of the images. The results show that using HDR images as input to detection
algorithms improves performance and that SfHDR and HfHDR enhance FP
description.