Brendan Chwyl

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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)
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)
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)
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