Real-Time Global Motion Vectors Estimation Based on Phase Correlation and Gray Projection Algorithm

Author(s):  
Yangke Liu ◽  
De Jifu ◽  
Bo Li ◽  
Qizhi Xu
2021 ◽  
Vol 40 (2) ◽  
pp. 79-90
Author(s):  
Zheng Zeng ◽  
Shiqiu Liu ◽  
Jinglei Yang ◽  
Lu Wang ◽  
Ling‐Qi Yan
Keyword(s):  

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.


2012 ◽  
Vol 107 (12) ◽  
pp. 3493-3508 ◽  
Author(s):  
Kaoru Amano ◽  
Tsunehiro Takeda ◽  
Tomoki Haji ◽  
Masahiko Terao ◽  
Kazushi Maruya ◽  
...  

Early visual motion signals are local and one-dimensional (1-D). For specification of global two-dimensional (2-D) motion vectors, the visual system should appropriately integrate these signals across orientation and space. Previous neurophysiological studies have suggested that this integration process consists of two computational steps (estimation of local 2-D motion vectors, followed by their spatial pooling), both being identified in the area MT. Psychophysical findings, however, suggest that under certain stimulus conditions, the human visual system can also compute mathematically correct global motion vectors from direct pooling of spatially distributed 1-D motion signals. To study the neural mechanisms responsible for this novel 1-D motion pooling, we conducted human magnetoencephalography (MEG) and functional MRI experiments using a global motion stimulus comprising multiple moving Gabors (global-Gabor motion). In the first experiment, we measured MEG and blood oxygen level-dependent responses while changing motion coherence of global-Gabor motion. In the second experiment, we investigated cortical responses correlated with direction-selective adaptation to the global 2-D motion, not to local 1-D motions. We found that human MT complex (hMT+) responses show both coherence dependency and direction selectivity to global motion based on 1-D pooling. The results provide the first evidence that hMT+ is the locus of 1-D motion pooling, as well as that of conventional 2-D motion pooling.


Sign in / Sign up

Export Citation Format

Share Document