2014/06/24 by Reiner Lenz, Lenz, Reiner
Computer Science · Mathematics · Medicine · #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Contrast (vision) #Distribution (mathematics) #Euclidean geometry #Euclidean space #Eye movement #Eye tracking #FOS: Computer and information sciences #Fixation (population genetics) #Gaze Tracking and Assistive Technology #Generalized Pareto distribution #Geometry #Glaucoma and retinal disorders #Hyperbolic space #I.5.4 #Mathematical analysis #Mathematical optimization #Mathematics #Measure (data warehouse) #Pareto principle #Saccadic masking #Statistics #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.1406.6201
published in arXiv (Cornell University) (Cornell University)
arxiv created 2014/06/24 · openalex publication_date 2014/06/24 · arxiv updated 2014/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We describe a statistical analysis of the eye tracker measurements in a database with 15 observers viewing 1003 images under free-viewing conditions. In contrast to the common approach of investigating the properties of the fixation points we analyze the properties of the transition phases between fixations. We introduce hyperbolic geometry as a tool to measure the step length between consecutive eye positions. We show that the step lengths, measured in hyperbolic and euclidean geometry, follow a generalized Pareto distribution. The results based on the hyperbolic distance are more robust than those based on euclidean geometry. We show how the structure of the space of generalized Pareto distributions can be used to characterize and identify individual observers.