Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김기범 | - |
dc.date.accessioned | 2023-04-25T01:13:04Z | - |
dc.date.available | 2023-04-25T01:13:04Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | Proceedings of 18th International Bhurban Conference on Applied Sciences and Technologies, IBCAST 2021, article no. 9393166, Page. 518-523 | - |
dc.identifier.issn | 2151-1403;2151-1411 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9393166 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/179194 | - |
dc.description.abstract | in this paper, we highlighted object localization and recognition using RGB-D images that is top of RGB scenarios and provide semantically richer pixel-level support aps for individual object. Indeed, depth information levels with disparity-range of various objects in an image are used to extract objects of interest. Using proposed methodology, we extract point clouds from a depth image to proper plane fitting using Random Sample Consensus (RANSAC). RANSAC is challenging to handle the contour with thin edges. After local segmentation, we extracts various features like HOG and shape cues values to explore spatial properties of each object class. For object classification, we applied two well-known classifiers i.e., random forest (RF) and linear SVM. In the experimental evaluation, we achieved a gain of 16% relative improvement over current state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring and sports scenes. © 2021 IEEE. | - |
dc.description.sponsorship | ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2018R1D1A1A02085645). | - |
dc.language | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject | Depth sensors | - |
dc.subject | Object detection | - |
dc.subject | Plane fitting | - |
dc.subject | Point-Clouds | - |
dc.subject | Random sample consensus | - |
dc.title | RGB-D Images for Objects Recognition using 3D Point Clouds and RANSAC Plane Fitting | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/IBCAST51254.2021.9393166 | - |
dc.relation.page | 518-523 | - |
dc.relation.journal | Proceedings of 18th International Bhurban Conference on Applied Sciences and Technologies, IBCAST 2021 | - |
dc.contributor.googleauthor | Jalal, Ahmad | - |
dc.contributor.googleauthor | Sarwar, M. Zeeshan | - |
dc.contributor.googleauthor | Kim, Kibum | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | ICT융합학부 | - |
dc.identifier.pid | kibum | - |
dc.identifier.article | 9393166 | - |
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