2017/11/09 by Wolfgang Fuhl, Fuhl, Wolfgang, Thiago Santini +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Holography and Microscopy #FOS: Computer and information sciences #I.4.5 #I.4.6 #I.4.7 #I.4.8 #Image Processing Techniques and Applications
paper · pdf · doi:10.48550/arxiv.1711.03306
openalex publication_date 2017/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many cameras implement auto-focus functionality. However, they typically require the user to manually identify the location to be focused on. While such an approach works for temporally-sparse autofocusing functionality (e.g., photo shooting), it presents extreme usability problems when the focus must be quickly switched between multiple areas (and depths) of interest - e.g., in a gaze-based autofocus approach. This work introduces a novel, real-time auto-focus approach based on eye-tracking, which enables the user to shift the camera focus plane swiftly based solely on the gaze information. Moreover, the proposed approach builds a graph representation of the image to estimate depth plane surfaces and runs in real time (requiring ~20ms on a single i5 core), thus allowing for the depth map estimation to be performed dynamically. We evaluated our algorithm for gaze-based depth estimation against state-of-the-art approaches based on eight new data sets with flat, skewed, and round surfaces, as well as publicly available datasets.