Evaluation of Feature Detection in HDR Based Imaging Under Changes in Illumination Conditions

Abstract

High dynamic range (HDR) imaging enables to capture details in both dark and very bright regions of a scene, and is therefore supposed to provide higher robustness to illumination changes than conventional low dynamic range (LDR) imaging in tasks such as visual features extraction. However, it is not clear how much this gain is, and which are the best modalities of using HDR to obtain it. In this paper we evaluate the first block of the visual feature extraction pipeline, i.e., keypoint detection, using both LDR and different HDR-based modalities, when significant illumination changes are present in the scene. To this end, we captured a dataset with two scenes and a wide range of illumination conditions. On these images, we measure how the repeatability of either corner or blob interest points is affected with different LDR/HDR approaches. Our observations confirm the potential of HDR over conventional LDR acquisition. Moreover, extracting features directly from HDR pixel values is more effective than first tonemapping and then extracting features, provided that HDR luminance information is previously encoded to perceptually linear values.