Lee, Seokyeong Lee, Dongjin Kim, Hyun-Cheol Lee, Seungkyu 2024-01-19T13:01:21Z 2024-01-19T13:01:21Z 2022-04-05 2022-01 2169-3536 https://pubs.kist.re.kr/handle/201004/115830 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. English Institute of Electrical and Electronics Engineers Inc. Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation Article 10.1109/ACCESS.2021.3137585 1 IEEE Access, v.10, pp.134 - 143 IEEE Access 10 134 143 Y scie scopus 000739475800001 2-s2.0-85122065423 Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Telecommunications Article DISCRIMINATIVE ILLUMINATION OPTIMAL PROJECTIONS CLASSIFICATION Cameras Image color analysis Estimation Surface roughness Rough surfaces Three-dimensional displays Surface reconstruction Material type surface reflectance