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Discrimination is optimal at the midpoint of the receptor response function, and linear von Kries adaptation is predicted to keep this midpoint at the intensity of the adaptive background.
Kalman filter is used for adaptive background estimation in [7].
In [11], two techniques allowing adaptive background modeling are presented.
Background modeling and moving object detection are based on the adaptive background subtraction method.
Our segmentation method is an extension of standard motion detection methods with adaptive background modeling.
In non-stationary noise cases, there can be used an adaptive background noise spectrum estimation procedure [67].
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In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment.
To address these issues, a hierarchical background modeling and subtraction [3] and a self-adaptive background matching method [4] have been proposed.
A novel self-adaptive background current compensation circuit applied to infrared focal plane array is proposed in this paper, which can compensate the background current generated in different conditions.
To solve the above problem, a new self-adaptive background approximating and updating algorithm based on optical flow theory is presented for the traffic surveillance scene in this paper.
We adopt the adaptive Gaussian background model to obtain the foreground target and update the background model synchronously.
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