We introduced a new method called divided-aperture dual-differential confocal microscopy (DADDCM), which delivered large sensing measurement range and high axial focusing capability for profile measurement. There are three virtual pinholes, one is on the optical axis and the other two are placed either side of the optical axis. The signal from each off-axis will be processed with the on-axis one, and the processed signals are added up to acquire the axial intensity response curve with large linear sensing range. So, it can realize the large-scale non-axial fast sensing scanning with an axial focusing capability of ~2 nm and an improved linear sensing range up to 2.1 times that of divided-aperture differential confocal microscopy (DADCM). Benefiting from this large linear sensing range, a non-axial scanning imaging detection of microstructures is implemented, which leads to a high scanning speed. This method provides a new high precision and fast measurement method for the three-dimensional morphology of microstructure.
Imaging Photoplethysmography (IPPG) is an emerging technique for the extraction of vital signs of human being using video recordings. IPPG technology with its advantages like non-contact measurement, low cost and easy operation has become one research hot spot in the field of biomedicine. However, the noise disturbance caused by non-microarterial area cannot be removed because of the uneven distribution of micro-arterial, different signal strength of each region, which results in a low signal noise ratio of IPPG signals and low accuracy of heart rate.
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.
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