{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T18:32:52Z","timestamp":1725042772133},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T00:00:00Z","timestamp":1503532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013463","name":"KRISO","doi-asserted-by":"publisher","award":["PES9000"],"id":[{"id":"10.13039\/501100013463","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"MOTIE","doi-asserted-by":"publisher","award":["PNS2980"],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status\u2014i.e., the existence and identity (or name)\u2014of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods\u2014particle filtering and Bayesian feature estimation\u2014are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented.<\/jats:p>","DOI":"10.3390\/s17091953","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T12:44:54Z","timestamp":1503578694000},"page":"1953","source":"Crossref","is-referenced-by-count":11,"title":["Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images \u2020"],"prefix":"10.3390","volume":"17","author":[{"given":"Yeongjun","family":"Lee","sequence":"first","affiliation":[{"name":"Marine Robotics Laboratory, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Korea"}]},{"given":"Jinwoo","family":"Choi","sequence":"additional","affiliation":[{"name":"Marine Robotics Laboratory, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4277-3450","authenticated-orcid":false,"given":"Nak","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Chosun University, Gwangju 61452, Korea"}]},{"given":"Hyun-Taek","family":"Choi","sequence":"additional","affiliation":[{"name":"Marine Robotics Laboratory, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Carlevaris-Bianco, N., Mohan, A., and Eustice, R.M. 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