It is also desirable to recover information on missing aftershocks using modeling of detection rates, particularly in the early stage of aftershocks (Omi et al. 2013, 2014a, b).
Our CSF results are also relevant to an existing literature on "eye-filtered" models of detection (Bouwman, van Engen, Dance, Young, & Veldkamp, 2014; Burgess, 1994; Monnin, Bochud, & Verdun, 2010; Monnin, Marshall, Bosmans, Bochud, & Verdun, 2011; Van Peteghem, Bosmans, & Marshall, 2016) These models use a CSF function to modify a simple matched-filter target detector.
Thus, we cannot estimate the performance of the Predict models if the mode of detection were known.
Based on an improved learning mechanism, TLD [34] constantly updates the feature points of the tracking module and the target model of the detection module, making detection and tracking more stable and reliable.
A test for trend using the p value of the effect of year from the binary logistic regression model of the detection method (mammography detected yes/no) on year was significant, p <.001.
The results confirm that the present model of object detection, based on CSC, outperforms state-of-the-art of shape-based detection methods.
In this study, a simulator for the reference model of fire detection and monitoring system was developed.
We also propose a multi-channel model of symmetry detection that combines the output of oriented spatial filters in a simple and physiologically plausible manner.
Here, we present a neural network model of motion detection, figure ground segregation and attentive selection which explains these response patterns in an unifying framework.
