OBJECTIVE ASSESSMENT OF VIDEO SEGMENTATION QUALITY FOR AUGMENTED REALITY

Assessment of video segmentation quality is a problem seldom investigated by the scientific community. Nevertheless, recent studies presented some objective metrics to evaluate algorithms. Such metrics consider different ways in which segmentation errors occur (perceptual factors) and its parameters are adjusted according to the application for which the segmented frames are intended. We demonstrate empirically that the performance of existing metrics changes according to the segmentation algorithm; we applied such metrics to evaluate bilayer segmentation algorithms for compose scenes in Augmented Reality environments. We also contribute with a new objective metric to adjust the parameters of two bilayer segmentation algorithms found in the literature.