Open AccessIPDDF: an improved precision dense descriptor based flow estimation
Author(s) -
Eng Weiyong,
Koo Voonchet,
Lim Tiensze
Publication year - 2020
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2019.0052
Subject(s) - pixel , matching (statistics) , computation , histogram , orientation (vector space) , artificial intelligence , computer science , filter (signal processing) , optical flow , flow (mathematics) , computer vision , field (mathematics) , algorithm , mathematics , pattern recognition (psychology) , image (mathematics) , statistics , geometry , pure mathematics
Large displacement optical flow algorithms are generally categorised into descriptor‐based matching and pixel‐based matching. Descriptor‐based approaches are robust to geometric variation, however they have inherent localisation precision limitation due to histogram nature. This work presents a novel method called improved precision dense descriptor flow (IPDDF). The authors introduce an additional pixel‐based matching cost within an existing dense Daisy descriptor framework to improve the flow estimation precision. Pixel‐based features such as pixel colour and gradient are computed on top of the original descriptor in the authors' matching cost formulation. The pixel‐based cost only requires a light‐weight pre‐computation and can be adapted seamlessly into the matching cost formulation. The framework is built based on the Daisy Filter Flow work. In the framework, Daisy descriptor and a filter‐based efficient flow inference technique, as well as a randomised fast patch match search algorithm, are adopted. Given the novel matching cost formulation, the framework enables efficiently solving dense correspondence field estimation in a high‐dimensional search space, which includes scale and orientation. Experiments on various challenging image pairs demonstrate the proposed algorithm enhances flow estimation accuracy as well as generate a spatially coherent yet edge‐aware flow field result efficiently.