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dc.contributor.author김기범-
dc.date.accessioned2021-07-22T05:12:15Z-
dc.date.available2021-07-22T05:12:15Z-
dc.date.issued2020-03-
dc.identifier.citation2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), page. 271-276en_US
dc.identifier.isbn978-1-7281-4675-1-
dc.identifier.isbn978-1-7281-4676-8-
dc.identifier.issn2151-1411-
dc.identifier.urihttps://ieeexplore.ieee.org/document/9044576?arnumber=9044576&SID=EBSCO:edseee-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/163049-
dc.description.abstractWith the advancement of technology, intelligence capabilities of machines are growing day by day. Researchers are committed to equip the machines with the capability of thinking humanly. Currently, the machines can sense and process information gathered from sensors. However, still there is a huge gape to improve the capability of thinking and understanding real scenes. Scene understanding is fiery area of research now a day. Therefore, we have proposed a model to understand and recognize a scene using depth data to make machines capable of interpreting the real time scenes like humans. The proposed recognition technique is a novel segmentation framework that uses statistical multi object segmentation to learn robust scene model and segregate the objects in the scene. Then, the unique features are extracted from these segregated objects to further process for recognition using linear SVM. Finally, multilayer perceptron is provided with the features and weights for the recognition of the scene. Our system demonstrated significant improvement over state-of-the-art systems. The proposed system is effective in autonomous vision systems like robotic vision, GPS based location finder, sports and security.en_US
dc.description.sponsorshipThis research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1D1A1A02085645).en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectDepth imagesen_US
dc.subjectmulti-object detectionen_US
dc.subjectstatistical segmentationen_US
dc.subjectscene detectionen_US
dc.titleStatistical Multi-Objects Segmentation for Indoor/Outdoor Scene Detection and Classification via Depth Imagesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/IBCAST47879.2020.9044576-
dc.relation.page271-276-
dc.contributor.googleauthorRafique, Adnan Ahmed-
dc.contributor.googleauthorJalal, Ahmad-
dc.contributor.googleauthorKim, Kibum-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY-
dc.identifier.pidkibum-
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