scholarly journals The contribution of scene gist and spatial dependency of objects to semantic guidance of attention

2013 ◽  
Vol 13 (9) ◽  
pp. 1120-1120
Author(s):  
C.-C. Wu ◽  
H.-C. Wang ◽  
M. Pomplun
Keyword(s):  
Spatial Dependency ◽  
Semantic Guidance ◽  
Scene Gist

scholarly journals Spatial dependency of objects, but not scene gist contributes semantic guidance of attention

2014 ◽  
Vol 14 (10) ◽  
pp. 1199-1199
Author(s):  
C.-C. Wu ◽  
H.-C. Wang ◽  
M. Pomplun
Keyword(s):  
Spatial Dependency ◽  
Semantic Guidance ◽  
Scene Gist

Spatial dependency in local resource distributions.

2018 ◽  
Vol 12 (3) ◽  
pp. 163-172 ◽  
Author(s):  
Andreas Wilke ◽  
Josie Lydick ◽  
Valaree Bedell ◽  
Taylor Dawley ◽  
Jordan Treat ◽  
...  
Keyword(s):  
Spatial Dependency ◽  
Local Resource ◽  
Resource Distributions

Scene gist processing and its mechanisms

2018 ◽  
Vol 26 (1) ◽  
pp. 81
Author(s):  
LI Mengqi ◽  
CHEN Zhimin ◽  
ZHENG Yuanjie ◽  
REN Yanju
Keyword(s):  
Scene Gist

scholarly journals Cox Processes Associated with Spatial Copula Observed through Stratified Sampling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Walguen Oscar ◽  
Jean Vaillant
Keyword(s):  
Environmental Effects ◽  
Gaussian Copula ◽  
Spatial Dependency ◽  
Spatial Effects ◽  
Doubly Stochastic ◽  
Cox Processes ◽  
Black Leaf Streak ◽  
Spatial Random Field ◽  
Black Leaf Streak Disease ◽  
Spatial Count Data

Cox processes, also called doubly stochastic Poisson processes, are used for describing phenomena for which overdispersion exists, as well as Poisson properties conditional on environmental effects. In this paper, we consider situations where spatial count data are not available for the whole study area but only for sampling units within identified strata. Moreover, we introduce a model of spatial dependency for environmental effects based on a Gaussian copula and gamma-distributed margins. The strength of dependency between spatial effects is related with the distance between stratum centers. Sampling properties are presented taking into account the spatial random field of covariates. Likelihood and Bayesian inference approaches are proposed to estimate the effect parameters and the covariate link function parameters. These techniques are illustrated using Black Leaf Streak Disease (BLSD) data collected in Martinique island.

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