# CP HDR: A feature point detection and description library for LDR and HDR images

arXiv: https://arxiv.org/abs/2403.19935 · HTML: https://mlsanity.com/paper/2403.19935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 77 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content](https://mlsanity.com/paper/1803.02266/) (2018)
- [Feature point detection in HDR images based on coefficient of variation](https://mlsanity.com/paper/2304.10666/) (2023)

## Abstract (arXiv)

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.