Modeling motion flow using tensor dynamic textures

2016 ◽  
Author(s):  
Bingyin Zhou ◽  
Qingyun Ren ◽  
Ming Lu
Keyword(s):  
Dynamic Textures ◽  
Motion Flow

Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences

2020 ◽  
Vol 65 (24) ◽  
pp. 245020
Author(s):  
Huihui Fang ◽  
Heng Li ◽  
Shuang Song ◽  
Kun Pang ◽  
Danni Ai ◽  
...  
Keyword(s):  
Recurrent Network ◽  
Image Sequences ◽  
Signal Estimation ◽  
X Ray ◽  
Respiratory Signal ◽  
Angiographic Image ◽  
Motion Flow

scholarly journals Characterizing Spatters in Laser Welding of Thick Steel Using Motion Flow Analysis

2013 ◽  
pp. 675-686 ◽  
Author(s):  
Olli Lahdenoja ◽  
Tero Säntti ◽  
Jonne Poikonen ◽  
Mika Laiho ◽  
Ari Paasio
Keyword(s):  
Laser Welding ◽  
Flow Analysis ◽  
Thick Steel ◽  
Motion Flow

Prescribed motion flow dynamics

2019 ◽  
Vol 33 (1) ◽  
pp. 289-297
Author(s):  
Md Zishan Akhter ◽  
Ravi Chaithanya Mysa
Keyword(s):  
Flow Dynamics ◽  
Motion Flow

scholarly journals High Order Volumetric Directional Pattern for Video-Based Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Almabrok Essa ◽  
Vijayan Asari
Keyword(s):  
State Of The Art ◽  
Directional Pattern ◽  
High Order ◽  
Dynamic Textures ◽  
Dynamic Features ◽  
Novel Approach ◽  
Variation Patterns ◽  
Directional Variation ◽  
Video Based Face Recognition ◽  
Information Dynamic

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches.

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