{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/appd-adaptive-and-precise-pupil-boundary","title":"APPD: Adaptive and Precise Pupil Boundary Detection using Entropy of Contour Gradients","arxiv_id":"1709.06366","date":"2017-09-19","proceeding":null,"authors":["Cihan Topal","Halil Ibrahim Cakir","Cuneyt Akinlar"],"abstract":"Eye tracking spreads through a vast area of applications from ophthalmology,\nassistive technologies to gaming and virtual reality. Precisely detecting the\npupil's contour and center is the very first step in many of these tasks, hence\nneeds to be performed accurately. Although detection of pupil is a simple\nproblem when it is entirely visible; occlusions and oblique view angles\ncomplicate the solution. In this study, we propose APPD, an adaptive and\nprecise pupil boundary detection method that is able to infer whether entire\npupil is in clearly visible by a heuristic that estimates the shape of a\ncontour in a computationally efficient way. Thus, a faster detection is\nperformed with the assumption of no occlusions. If the heuristic fails, a more\ncomprehensive search among extracted image features is executed to maintain\naccuracy. Furthermore, the algorithm can find out if there is no pupil as an\nhelpful information for many applications. We provide a dataset containing 3904\nhigh resolution eye images collected from 12 subjects and perform an extensive\nset of experiments to obtain quantitative results in terms of accuracy,\nlocalization and timing. The proposed method outperforms three other state of\nthe art algorithms and has an average execution time $\\sim$5 ms in\nsingle-thread on a standard laptop computer for 720p images.","url_abs":"http://arxiv.org/abs/1709.06366v2","url_pdf":"http://arxiv.org/pdf/1709.06366v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"appd-adaptive-and-precise-pupil-boundary","repo_url":"https://github.com/LeszekSwirski/pupiltracker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"boundary-detection","task_name":"Boundary Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}