TY - GEN
T1 - Preserving temporal consistency in videos through adaptive SLIC
AU - Zhang, Han
AU - Ali, Riaz
AU - Sheng, Bin
AU - Li, Ping
AU - Kim, Jinman
AU - Wang, Jihong
N1 - Funding Information:
Acknowledgement. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFF0300903, in part by the National Natural Science Foundation of China under Grant 61872241 and Grant 61572316, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 15490503200, Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - The application of image processing techniques to individual frames of video often results in temporal inconsistency. Conventional approaches used for preserving the temporal consistency in videos have shortcomings as they are used for only particular jobs. Our work presents a multipurpose video temporal consistency preservation method that utilizes an adaptive simple linear iterative clustering (SLIC) algorithm. First, we locate the inter-frame correspondent pixels through the SIFT Flow and use them to find the respective regions. Then, we apply a multiframe matching statistical method to get the spatially or temporally correspondent frames. Besides, we devise a least-squares energy-based flickering-removing objective function by taking into account the inter-frame temporal consistency and inter-region spatial consistency jointly. The obtained results demonstrate the potential of the proposed method.
AB - The application of image processing techniques to individual frames of video often results in temporal inconsistency. Conventional approaches used for preserving the temporal consistency in videos have shortcomings as they are used for only particular jobs. Our work presents a multipurpose video temporal consistency preservation method that utilizes an adaptive simple linear iterative clustering (SLIC) algorithm. First, we locate the inter-frame correspondent pixels through the SIFT Flow and use them to find the respective regions. Then, we apply a multiframe matching statistical method to get the spatially or temporally correspondent frames. Besides, we devise a least-squares energy-based flickering-removing objective function by taking into account the inter-frame temporal consistency and inter-region spatial consistency jointly. The obtained results demonstrate the potential of the proposed method.
KW - Adaptive SLIC
KW - Temporal consistency
KW - Video processing
UR - http://www.scopus.com/inward/record.url?scp=85096524427&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61864-3_34
DO - 10.1007/978-3-030-61864-3_34
M3 - Conference article published in proceeding or book
AN - SCOPUS:85096524427
SN - 9783030618636
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 405
EP - 410
BT - Advances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Stephanidis, Constantine
A2 - Papagiannakis, George
A2 - Wu, Enhua
A2 - Thalmann, Daniel
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 37th Computer Graphics International Conference, CGI 2020
Y2 - 20 October 2020 through 23 October 2020
ER -