  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.
