Brendan Chwyl

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The reconstruction of high dynamic range (HDR) images via conventional camera systems and low dynamic range (LDR) images is a growing field of research in image acquisition. The radiance map associated with the HDR image of a scene is typically computed using multiple images of the same scene captured at different exposures (i.e., bracketed LDR imzages).(More)
Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this(More)
A novel stochastic Bayesian estimation method is introduced for the purpose of suppressing specular reflectance in endoscopic imagery, benefiting both computer aided and manual analysis of endoscopic data. The maximum diffuse chromaticity, which is necessary for the calculation of the specular reflectance, is estimated via Bayesian least-squares(More)
A novel method for remote heart rate estimation via analysis in the time-frequency domain is proposed. A photoplethysmogram (PPG) waveform is constructed via a Bayesian minimization approach with the required posterior probability obtained through an importance-weighted Monte Carlo sampling method. A pulselet (wavelet chosen for its similarities with a(More)
A novel method, Stochastically Acquired Photoplethysmo-gram for Heart rate Inference in Realistic Environments (SAPPHIRE), is proposed for robust remote heart rate measurement through broadband video. A set of stochastically sampled points from the cheek region is tracked and used to construct corresponding time series observations via skin erythema(More)
A method for illumination robust facial feature detection on frontal images of the human face is proposed. Illumination robust features are produced from weighted contributions of the texture and illumination components of an image where the illumination is estimated via Bayesian least-squares minimization with the required posterior probability inferred(More)
Global saliency is an important aspect of many computerand robotic vision tasks, and with the increased interest infields such as autonomous navigation, a significant area ofresearch. A challenging aspect of modelling global saliencyin practical applications is the presence of varying or non-uniform illumination conditions. Many current models fail(More)
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