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Feature point (FP) detection is a fundamental step of many computer visiontasks. However, FP detectors are usually designed for low dynamic range (LDR)images. In scenes with extreme light conditions, LDR images present saturatedpixels, which degrade FP detection. On the other hand, high dynamic range (HDR)images usually present no saturated pixels but FP detection algorithms do nottake advantage of all the information present in such images. FP detectionfrequently relies on differential methods, which work well in LDR images.However, in HDR images, the differential operation response in bright areasovershadows the response in dark areas. As an alternative to standard FPdetection methods, this study proposes an FP detector based on a coefficient ofvariation (CV) designed for HDR images. The CV operation adapts its responsebased on the standard deviation of pixels inside a window, working well in bothdark and bright areas of HDR images. The proposed and standard detectors areevaluated by measuring their repeatability rate (RR) and uniformity. Ourproposed detector shows better performance when compared to other standardstate-of-the-art detectors. In uniformity metric, our proposed detectorsurpasses all the other algorithms. In other hand, when using the repeatabilityrate metric, the proposed detector is worse than Harris for HDR and SURFdetectors.

Feature point detection in HDR images based on coefficient of variation