{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:38:31Z","timestamp":1771612711574,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"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>Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.<\/jats:p>","DOI":"10.3390\/jimaging7090188","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T21:23:29Z","timestamp":1631913809000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Low Redundancy Wavelet Entropy Edge Detection Algorithm"],"prefix":"10.3390","volume":"7","author":[{"given":"Yiting","family":"Tao","sequence":"first","affiliation":[{"name":"UniSA STEM, Mawson Lakes Campus, University of South Australia, Adelaide, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Scully","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2610, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-3943","authenticated-orcid":false,"given":"Asanka G.","family":"Perera","sequence":"additional","affiliation":[{"name":"UniSA STEM, Mawson Lakes Campus, University of South Australia, Adelaide, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-4446","authenticated-orcid":false,"given":"Andrew","family":"Lambert","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2610, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-0543","authenticated-orcid":false,"given":"Javaan","family":"Chahl","sequence":"additional","affiliation":[{"name":"UniSA STEM, Mawson Lakes Campus, University of South Australia, Adelaide, SA 5095, Australia"},{"name":"Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86096","DOI":"10.1109\/ACCESS.2021.3086103","article-title":"Chest X-ray Outlier Detection Model using Dimension Reduction and Edge Detection","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1080\/13658816.2019.1650363","article-title":"Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images","volume":"34","author":"Dai","year":"2020","journal-title":"Int. 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