{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:10:09Z","timestamp":1760220609196,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2012,8,13]],"date-time":"2012-08-13T00:00:00Z","timestamp":1344816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Terrain classification allows a mobile robot to create an annotated map of its local environment from the three-dimensional (3D) and two-dimensional (2D) datasets collected by its array of sensors, including a GPS receiver, gyroscope, video camera, and range sensor. However, parts of objects that are outside the measurement range of the range sensor will not be detected. To overcome this problem, this paper describes an edge estimation method for complete scene recovery and complete terrain reconstruction. Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds. Further, a masking method is proposed to classify buildings and trees in a terrain mesh.<\/jats:p>","DOI":"10.3390\/s120811221","type":"journal-article","created":{"date-parts":[[2012,8,13]],"date-time":"2012-08-13T11:07:25Z","timestamp":1344856045000},"page":"11221-11237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes"],"prefix":"10.3390","volume":"12","author":[{"given":"Wei","family":"Song","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 26 Pildong 3 Ga, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2219-0848","authenticated-orcid":false,"given":"Kyungeun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 26 Pildong 3 Ga, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyhyun","family":"Um","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 26 Pildong 3 Ga, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chee Sun","family":"Won","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University-Seoul, 26 Pildong 3 Ga, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungdae","family":"Sim","sequence":"additional","affiliation":[{"name":"Agency for Defense Development, Bugyuseong daero 488 beon gi, Yoseong, Daejeon 305-152, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.robot.2011.02.009","article-title":"On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter","volume":"59","author":"Matsushita","year":"2011","journal-title":"Robot. 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