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A piston detection method for segments via convolutional neural networks and its robustness analysis
In this paper, we propose a method of improving the signal noise ratio of camera-based IPPG signals of each sub-region of the face using a weighted average. Firstly, we obtain the region of interest (ROI) of a subject’s face based camera. Secondly, each region of interest is tracked and feature-based matched in each frame of the video. Each tracked region of face is divided into 60x60 pixel block. Thirdly, the weights of PPG signal of each sub-region are calculated, based on the signal-to-noise ratio of each sub-region. Finally, we combine the IPPG signal from all the tracked ROI using weighted average. Compared with the existing approaches, the result shows that the proposed method takes modest but significant effects on improvement of signal noise ratio of camera-based PPG estimated and accuracy of heart rate measurement.
To evaluate the influence on the target imaging made by the IRW radiation, the experiment on the basis of building a simulating model is conducted by the means of ray tracing so that the accurate transmittance of the IRW can be observed under the different temperature. And then the radiation distribution of the thermal radiation on the detector generated by the IRW radiation noise and target signal can finally be obtained.
This paper also records the different parameters in the detection system being set in the experiment, and analyzes the different influences brought by various factors to the Signal to Noise Ratio (SNR). It is also expected that it will provide a data reference to the following research of radiation noise suppression and design of IR detection system.
A light efficiency uniformity detection system for medical rigid endoscope based on image processing
Corresponding characteristics of the alignment parameters in large segmented-mirror telescope system
New method for detecting the piston of segmented mirrors: a modification of the peak ratio technique
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