{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T04:06:36Z","timestamp":1762056396546,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Given a spherical set of points, we consider the detection of cocircular subsets of the data. We distinguish great circles from small circles, and develop algorithms for detecting cocircularities of both types. The suggested approach is an extension of the Hough transform. We address the unique parameter-space quantization issues arising due to the spherical geometry, present quantization schemes, and evaluate the quantization-induced errors. We demonstrate the proposed algorithms by detecting cocircular cities and airports on Earth\u2019s spherical surface. These results facilitate the detection of great and small circles in spherical images.<\/jats:p>","DOI":"10.3390\/jimaging8070184","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T10:21:33Z","timestamp":1657016493000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Cocircular Subsets of a Spherical Set of Points"],"prefix":"10.3390","volume":"8","author":[{"given":"Basel","family":"Ibrahim","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1436-2275","authenticated-orcid":false,"given":"Nahum","family":"Kiryati","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"ref_1","first-page":"381","article-title":"Random Sample Consensus: A paradigm for model fitting with applications to image analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. 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