TY - GEN
T1 - Semantic segmentation of indoor-scene RGB-D images based on iterative contraction and merging
AU - Syu, Jia Hao
AU - Cho, Shih Hsuan
AU - Wang, Sheng-Jyh
AU - Wang, Li-Chun
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - In this paper, we propose an iterative contraction and merging framework (ICM) for semantic segmentation in indoor scenes. Given an input image and a raw depth image, we first derive the dense prediction map from a convolutional neural network (CNN) and a normal vector map from the depth image. By combining the RGB-D image with these two maps, we can guide the ICM process to produce a more accurate hierarchical segmentation tree in a bottom-up manner. After that, based on the hierarchical segmentation tree, we design a decision process which uses the dense prediction map as a reference to make the final decision of semantic segmentation. Experimental results show that the proposed method can generate much more accurate object boundaries if compared to the state-of-the-art methods.
AB - In this paper, we propose an iterative contraction and merging framework (ICM) for semantic segmentation in indoor scenes. Given an input image and a raw depth image, we first derive the dense prediction map from a convolutional neural network (CNN) and a normal vector map from the depth image. By combining the RGB-D image with these two maps, we can guide the ICM process to produce a more accurate hierarchical segmentation tree in a bottom-up manner. After that, based on the hierarchical segmentation tree, we design a decision process which uses the dense prediction map as a reference to make the final decision of semantic segmentation. Experimental results show that the proposed method can generate much more accurate object boundaries if compared to the state-of-the-art methods.
KW - Convolutional neural network
KW - Iterative contraction and merging
KW - RGB-D image
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85049666545&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94211-7_28
DO - 10.1007/978-3-319-94211-7_28
M3 - Conference contribution
AN - SCOPUS:85049666545
SN - 9783319942100
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 261
BT - Image and Signal Processing - 8th International Conference, ICISP 2018, Proceedings
A2 - Mammass, Driss
A2 - Nouboud, Fathallah
A2 - Mansouri, Alamin
A2 - El Moataz, Abderrahim
PB - Springer Verlag
T2 - 8th International Conference on Image and Signal Processing, ICISP 2018
Y2 - 2 July 2018 through 4 July 2018
ER -