TY - GEN
T1 - Depth-aware image colorization network
AU - Chu, Wei Ta
AU - Hsu, Yu Ting
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - The color bleeding problem remains a challenging issue in image colorization. That is, different objects share the same color when they are nearby, leading to the boundary between objects looks unnatural. In this paper, we study how to combine depth information into a neural network and achieve better image colorization. The reasons to integrate depth information are twofold: (1) Depth information clearly provides boundary information between objects, and (2) depth information is commonly available as the development of RGB-D cameras. To the best of our knowledge, depth information was not considered in image colorization before. We evaluate the proposed method from both objective and subjective perspectives, and demonstrate that better colorization results can be obtained when depth information is further considered.
AB - The color bleeding problem remains a challenging issue in image colorization. That is, different objects share the same color when they are nearby, leading to the boundary between objects looks unnatural. In this paper, we study how to combine depth information into a neural network and achieve better image colorization. The reasons to integrate depth information are twofold: (1) Depth information clearly provides boundary information between objects, and (2) depth information is commonly available as the development of RGB-D cameras. To the best of our knowledge, depth information was not considered in image colorization before. We evaluate the proposed method from both objective and subjective perspectives, and demonstrate that better colorization results can be obtained when depth information is further considered.
UR - http://www.scopus.com/inward/record.url?scp=85058304566&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058304566&partnerID=8YFLogxK
U2 - 10.1145/3267799.3267800
DO - 10.1145/3267799.3267800
M3 - Conference contribution
AN - SCOPUS:85058304566
T3 - EE-USAD 2018 - Proceedings of the 2018 Workshop on Understanding Subjective Attributes of Data, with the Focus on Evoked Emotions, co-located with MM 2018
SP - 17
EP - 23
BT - EE-USAD 2018 - Proceedings of the 2018 Workshop on Understanding Subjective Attributes of Data, with the Focus on Evoked Emotions, co-located with MM 2018
PB - Association for Computing Machinery, Inc
T2 - 2018 Workshop on Understanding Subjective Attributes of Data, with the Focus on Evoked Emotions, EE-USAD 2018, in conjunction with ACM Multimedia, MM 2018
Y2 - 22 October 2018
ER -