A noise reduction method for range images using local Gaussian observation model constrained to unit tangent vector equality
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, J.H. | - |
dc.contributor.author | Choi, K.N. | - |
dc.date.accessioned | 2021-07-28T03:40:15Z | - |
dc.date.available | 2021-07-28T03:40:15Z | - |
dc.date.issued | 2012-11 | - |
dc.identifier.issn | 1876-1100 | - |
dc.identifier.issn | 1876-1119 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47974 | - |
dc.description.abstract | We present a method for smoothing heavy noisy surfaces acquired by on-the-fly 3D imaging devices to obtain the stable curvature. The smoothing is performed in a way that finds centers of probability distributions which maximizes the likelihood of observed points with smooth constraints. The smooth constraints are derived from the unit tangent vector equality. This provides a way of obtaining smooth surfaces and stable curvatures. We achieve the smoothing by solving the regularized linear system. The unit tangent vector equality involves consideration of geometric symmetry and it minimizes the variation of differential values that are a factor of curvatures. The proposed algorithm has two apparent advantages. The first thing is that the surfaces in a scene with various signals to noise ratio are smoothed and then they can earn suitable curvatures. The second is that the proposed method works on heavy noisy surfaces, e.g., a stereo camera image. Experiments on range images demonstrate that the method yields the smooth surfaces from the input with various signals to noise ratio and the stable curvatures obtained from the smooth surfaces. | - |
dc.format.extent | 9 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.title | A noise reduction method for range images using local Gaussian observation model constrained to unit tangent vector equality | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-94-007-2598-0_51 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Electrical Engineering, v.107 LNEE, pp 485 - 493 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-81255144235 | - |
dc.citation.endPage | 493 | - |
dc.citation.startPage | 485 | - |
dc.citation.title | Lecture Notes in Electrical Engineering | - |
dc.citation.volume | 107 LNEE | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 독일 | - |
dc.subject.keywordAuthor | Linear system | - |
dc.subject.keywordAuthor | Local gaussian observation model | - |
dc.subject.keywordAuthor | Noise | - |
dc.subject.keywordAuthor | Range image | - |
dc.subject.keywordPlus | 3D imaging | - |
dc.subject.keywordPlus | Gaussians | - |
dc.subject.keywordPlus | Geometric symmetry | - |
dc.subject.keywordPlus | Noise | - |
dc.subject.keywordPlus | Noise ratio | - |
dc.subject.keywordPlus | Noise reduction methods | - |
dc.subject.keywordPlus | Noisy surface | - |
dc.subject.keywordPlus | Observation model | - |
dc.subject.keywordPlus | On-the-fly | - |
dc.subject.keywordPlus | Range images | - |
dc.subject.keywordPlus | Smooth constraints | - |
dc.subject.keywordPlus | Smooth surface | - |
dc.subject.keywordPlus | Stereo cameras | - |
dc.subject.keywordPlus | Tangent vectors | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Curve fitting | - |
dc.subject.keywordPlus | Information technology | - |
dc.subject.keywordPlus | Linear systems | - |
dc.subject.keywordPlus | Probability distributions | - |
dc.subject.keywordPlus | Telecommunication services | - |
dc.subject.keywordPlus | Three dimensional | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.