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Found 5 papers, 3 papers with code
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment
Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment.
Weighted combination and singular spectrum analysis based remote photoplethysmography pulse extraction in low-light environments
The test results verify that the proposed method has stronger robustness to low illumination than state-of-the-art methods, effectively improving the signal-to-noise ratio and heart rate estimation precision.
Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence.
Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
The prediction of epileptic seizure has always been extremely challenging in medical domain.
