Open AccessGeodesic‐based probability propagation for efficient optical flow
Author(s) -
Mei Ling,
Chen Zeyu,
Lai Jianhuang
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0394
Subject(s) - geodesic , probabilistic logic , markov random field , computer science , optical flow , benchmark (surveying) , belief propagation , markov chain , algorithm , displacement (psychology) , artificial intelligence , theoretical computer science , image (mathematics) , mathematics , machine learning , image segmentation , decoding methods , mathematical analysis , psychology , geodesy , psychotherapist , geography
Optical flow estimation has bottlenecks such as large displacement and motion blur. In this Letter, the authors propose a geodesic‐based probability propagation (GeoFlow) method combining the global geodesic with local spatial similarity to build a non‐local superpixel graph. To achieve efficient belief propagation, a probabilistic framework for optimising the Markov random field (MRF) objective is proposed. In this way, the limitation of local propagation can be tackled in the global image level, and the probabilistic framework reduces computational complexity in the optimisation. In experiments, their method showed promising performance by improving the results on two public large displacement benchmark datasets.