Evaluation of a Local Descriptor for HDR Images

Feature point (FP) detection and description are processes that detect and extract characteristics from images. Several computer vision applications rely on the usage of FPs. Most FP descriptors are designed to support low dynamic range (LDR) images as input. However, high dynamic range (HDR) images can show details in bright and shadowed areas that LDR images can not. For that reason, the interest in HDR imagery as input in the detection and description processes has been increasing. Previous studies have explored FP detectors in HDR images. However, none have presented FP descriptors designed for HDR images. This study compares the FP matching performance of description vectors generated from LDR and HDR images. The FPs were detected and described using a version of the SIFT algorithm adapted to support HDR images. The FP matching performance of the algorithm was evaluated with the mAP metric. In all cases, using HDR images increased the mAP values when compared to LDR images.