ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation
Cited 2 time in scopus Download 44 time Share share facebook twitter linkedin kakaostory
저자
이석영, 이동진, 김현철, 이승규
발행일
202201
출처
IEEE Access, v.10, pp.134-143
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2021.3137585
협약과제
21ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
초록
There are fundamental difficulties in obtaining material type of an arbitrary object using traditional sensors. Existing material type recognition methods mostly focus on color based visual features and object-prior. Surface reflectance is another critical clue in the characterization of certain material type and can be observed by traditional sensors such as color camera and time-of-flight depth sensor. A material type is characterized well by relevant surface reflectance together with traditional visual appearance providing better description for material type recognition. In this work, we propose a material type recognition method based on both color and reflectance features using deep neural network. Proposed method is evaluated on both public and our own data sets showing promising material type recognition results.
KSP 제안 키워드
Color camera, Data sets, Deep neural network(DNN), Depth sensor, Indoor scenes, Recognition method, Reflectance estimation, Visual appearance, Visual features, material type(Cellular), surface reflectance
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
저작자 표시 (CC BY)