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.

翻译：特征点检测是许多计算机视觉任务的基础步骤。然而，特征点检测器通常针对低动态范围图像设计。在极端光照场景下，低动态范围图像会出现饱和像素，从而降低特征点检测性能。而高动态范围图像通常不存在饱和像素，但现有特征点检测算法未能充分利用这类图像中的全部信息。特征点检测通常依赖差分方法，该方法在低动态范围图像中表现良好。但在高动态范围图像中，亮区域的差分响应会掩盖暗区域的响应。作为标准特征点检测方法的替代方案，本研究提出了一种基于变异系数的特征点检测器，专门针对高动态范围图像设计。变异系数运算能根据窗口内像素的标准差自适应调整响应，在高动态范围图像的暗区和亮区均能良好工作。通过重复率与均匀度指标评估了所提检测器与标准检测器的性能。与其他标准前沿检测器相比，所提检测器展现了更优的性能。在均匀度指标上，所提检测器超越了所有其他算法。而在重复率指标方面，所提检测器弱于面向高动态范围图像的Harris检测器及SURF检测器。