TY - GEN
T1 - Put things in correct location
T2 - 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
AU - Wu, Xingming
AU - Wang, Chenyang
AU - Chen, Weihai
AU - Liu, Zhong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/20
Y1 - 2015/11/20
N2 - In this paper, we reviewed the development of scene understanding, specifically focusing on indoor scene understanding and context used in scene understanding. Different from image segmentation, object detection, 3D reconstruction, etc., which only implements a single task, scene understanding is a technology aiming at implementing a holistic scene parsing, that is, acquiring the spatial extent, location and semantic of every object in a scene. Nevertheless, scene understanding doesn't equal to direct accumulation of individual vision tasks. Along with the development of scene understanding, it has formed its own framework. In general, indoor scene understanding is more challenging than outdoor scene understanding, but with the emergence of RGB-D sensors, more and more researchers have focused on indoor scene understanding. Fusing context information to constrain relationship between adjacent pixels and improve accuracy of algorithm is the common sense in the field of scene understanding. There are three kinds of context, namely, pixel based context, region based context and object based context. In this paper, We reviewed these different context respectively.
AB - In this paper, we reviewed the development of scene understanding, specifically focusing on indoor scene understanding and context used in scene understanding. Different from image segmentation, object detection, 3D reconstruction, etc., which only implements a single task, scene understanding is a technology aiming at implementing a holistic scene parsing, that is, acquiring the spatial extent, location and semantic of every object in a scene. Nevertheless, scene understanding doesn't equal to direct accumulation of individual vision tasks. Along with the development of scene understanding, it has formed its own framework. In general, indoor scene understanding is more challenging than outdoor scene understanding, but with the emergence of RGB-D sensors, more and more researchers have focused on indoor scene understanding. Fusing context information to constrain relationship between adjacent pixels and improve accuracy of algorithm is the common sense in the field of scene understanding. There are three kinds of context, namely, pixel based context, region based context and object based context. In this paper, We reviewed these different context respectively.
UR - https://www.scopus.com/pages/publications/84960893170
U2 - 10.1109/ICIEA.2015.7334081
DO - 10.1109/ICIEA.2015.7334081
M3 - 会议稿件
AN - SCOPUS:84960893170
T3 - Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
SP - 40
EP - 44
BT - Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 June 2015 through 17 June 2015
ER -