TY - GEN
T1 - Fast Non-Linear Methods for Dynamic Texture Prediction
AU - Katoch, Sameeksha
AU - Turaga, Pavan
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
N1 - Funding Information:
This work is funded in part by the NSF CPS Program #1646542 and the SenSIP Center.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate prediction method is presented which utilizes locality-sensitive hashing approaches to predict possible phase-space vectors, given the current phase-space vector. Our experiments show the favorable performance of the proposed approach, both in terms of prediction fidelity, and computational time. The proposed algorithm is applied to shading prediction in utility scale solar arrays.
AB - This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate prediction method is presented which utilizes locality-sensitive hashing approaches to predict possible phase-space vectors, given the current phase-space vector. Our experiments show the favorable performance of the proposed approach, both in terms of prediction fidelity, and computational time. The proposed algorithm is applied to shading prediction in utility scale solar arrays.
KW - Dynamic textures
KW - Phase-space reconstruction
KW - Shading prediction
KW - Solar energy
UR - http://www.scopus.com/inward/record.url?scp=85062844644&partnerID=8YFLogxK
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U2 - 10.1109/ICIP.2018.8451479
DO - 10.1109/ICIP.2018.8451479
M3 - Conference contribution
AN - SCOPUS:85062844644
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2107
EP - 2111
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
Y2 - 7 October 2018 through 10 October 2018
ER -