@inproceedings {64, title = {High-resolution motion-compensated photoplethysmographic imaging for remote heart rate monitoring}, booktitle = {SPIE Photonics West, Multimodal Biomedical Imaging X}, year = {2015}, month = {02/2015}, abstract = {

We present a novel non-contact photoplethysmographic (PPG) imaging system based on high-resolution video recordings of ambient reflectance of human bodies that compensates for body motion and takes advantage of skin erythema fluctuations to improve measurement reliability for the purpose of remote heart rate monitoring. A single measurement location for recording the ambient reflectance is automatically identified on an individual, and the motion for the location is determined over time via measurement location tracking. Based on the determined motion information motion-compensated reflectance measurements at different wavelengths for the measurement location can be acquired, thus providing more reliable measurements for the same location on the human over time. The reflectance measurement is used to determine skin erythema fluctuations over time, resulting in the capture of a PPG signal with a high signal-to-noise ratio. To test the efficacy of the proposed system, a set of experiments involving human motion in a front-facing position were performed under natural ambient light. The experimental results demonstrated that skin erythema fluctuations can achieve noticeably improved average accuracy in heart rate measurement when compared to previously proposed non-contact PPG imaging systems

}, keywords = {Imaging photoplethysmography, motion compensation, non-contact heart rate, skin erythema}, author = {A Chung and X Wang and R Amelard and C Scharfenberger and J Leong and A Wong and D A Clausi} } @inproceedings {254, title = {Model-based tracking: Temporal conditional random fields}, booktitle = {17th IEEE International Conference on Image Processing (ICIP)}, year = {2010}, abstract = {

We present Temporal Conditional Random Fields, a probabilistic framework for modeling object motion. The state-of-the-art discriminative approach for tracking is known as dynamic conditional random fields. This method models an event based on spatial and temporal relation between pixels in an image sequence without any prediction. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames) and line filed features. We validate our proposed method with real data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results indicate that our TCRF can predict future state of any maneuvering target with nearly zero error during its constant motion. Not only the proposed TCRF has a simple and easy to implement structure, but also it outperforms the state-of-the-art predictors such as Kalman filter.

}, keywords = {constant motion, CRF-based predictor, image sequence, image sequences, model-based tracking, motion compensation, object motion modeling, probabilistic framework, probability, temporal conditional random fields, tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2010.5653823}, author = {M J. Shafiee and Z Azimifar and P Fieguth} } @inproceedings {765, title = {Motion-compensated wavelet video denoising}, booktitle = {2004 International Conference on Image Analysis and Recognition}, year = {2004}, address = {Portugal}, abstract = {

We study a low complexity, motion-compensated recursive video denoising scheme in the wavelet domain. To preserve weak spatial textures, both spatial and temporal filtering are carried out in the wavelet domain. We justify the proposed approach by evaluating the relative increase of mean squared error (MSE) with respect to the optimal estimator. Various multiresolution motion estimation schemes are studied to allow us to exploit the high temporal correlation present in most video. Kalman filtering is then applied to the wavelet coefficients along motion trajectories to efficiently suppress noise.
We show experimentally that our wavelet-based recursive denoising compares favorably with other wavelet-based denoising approaches. Specifically, we can preserve both strong and weak spatial details while removing noise

}, keywords = {Kalman filtering, motion compensation, Video denoising, wavelet transform}, author = {F Jin and P Fieguth and L Winger} }