DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Soohwan | ko |
dc.contributor.author | Lee, Honggu | ko |
dc.contributor.author | Jo, Sung-Ho | ko |
dc.date.accessioned | 2015-03-27T07:51:44Z | - |
dc.date.available | 2015-03-27T07:51:44Z | - |
dc.date.created | 2014-12-23 | - |
dc.date.created | 2014-12-23 | - |
dc.date.issued | 2014-12 | - |
dc.identifier.citation | ELECTRONICS LETTERS, v.50, no.25, pp.1917 - 1918 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/194482 | - |
dc.description.abstract | Voxelisation methods are extensively employed for efficiently processing large point clouds. However, it is possible to lose geometric information and extract inaccurate features through these voxelisation methods. A novel, flexibly shaped 'supervoxel' algorithm, called boundary-enhanced supervoxel segmentation, for sparse and complex outdoor light detection and ranging (LiDAR) data is proposed. The algorithm consists of two key components: (i) detecting boundaries by analysing consecutive points and (ii) clustering the points by first excluding the boundary points. The generated super-voxels include spatial and geometric properties and maintain the shape of the object's boundary. The proposed algorithm is tested using sparse LiDAR data obtained from outdoor urban environments. | - |
dc.language | English | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Boundary-enhanced supervoxel segmentation for sparse outdoor LiDAR data | - |
dc.type | Article | - |
dc.identifier.wosid | 000345994700016 | - |
dc.identifier.scopusid | 2-s2.0-84928177184 | - |
dc.type.rims | ART | - |
dc.citation.volume | 50 | - |
dc.citation.issue | 25 | - |
dc.citation.beginningpage | 1917 | - |
dc.citation.endingpage | 1918 | - |
dc.citation.publicationname | ELECTRONICS LETTERS | - |
dc.identifier.doi | 10.1049/el.2014.3249 | - |
dc.contributor.localauthor | Jo, Sung-Ho | - |
dc.contributor.nonIdAuthor | Song, Soohwan | - |
dc.type.journalArticle | Article | - |
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