Feature point detection in HDR images based on coefficient of variation

TL;DR

This paper introduces a novel feature point detector based on the coefficient of variation tailored for HDR images, addressing saturation issues and improving uniformity over existing methods.

Contribution

The study proposes a CV-based FP detector specifically designed for HDR images, enhancing detection uniformity and adapting to varying brightness levels.

Findings

Outperforms standard detectors in uniformity metric.

Shows better overall performance than state-of-the-art detectors.

Less effective than Harris and SURF in repeatability rate.

Abstract

Feature point (FP) detection is a fundamental step of many computer vision tasks. However, FP detectors are usually designed for low dynamic range (LDR) images. In scenes with extreme light conditions, LDR images present saturated pixels, which degrade FP detection. On the other hand, high dynamic range (HDR) images usually present no saturated pixels but FP detection algorithms do not take advantage of all the information present in such images. FP detection frequently relies on differential methods, which work well in LDR images. However, in HDR images, the differential operation response in bright areas overshadows the response in dark areas. As an alternative to standard FP detection methods, this study proposes an FP detector based on a coefficient of variation (CV) designed for HDR images. The CV operation adapts its response based on the standard deviation of pixels inside a window, working well in both dark and bright areas of HDR images. The proposed and standard detectors are evaluated by measuring their repeatability rate (RR) and uniformity. Our proposed detector shows better performance when compared to other standard state-of-the-art detectors. In uniformity metric, our proposed detector surpasses all the other algorithms. In other hand, when using the repeatability rate metric, the proposed detector is worse than Harris for HDR and SURF detectors.