CGVQM is a full-reference video quality metric that predicts perceptual differences between pairs of videos. Like PSNR and SSIM, it compares a ground-truth reference to a distorted version (e.g.
Abstract: The purpose of this research is to create a system that can distinguish and correctly label different types of black and white buffaloes, such as Bonga, Saleko, Lotongboko, and buffaloes ...
Abstract: This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously ...
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