Matthias Hernandez

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We propose a method to produce laser scan quality 3-D face models from a freely moving user with a low-cost, low resolution depth camera. Our approach does not rely on any prior face model and can produce faithful geometric models of star-shaped objects. We represent the object in cylindrical coordinates, which enables us to perform filtering operations(More)
PURPOSE Geographic atrophy (GA) is the atrophic late-stage manifestation of age-related macular degeneration (AMD), which may result in severe vision loss and blindness. The purpose of this study was to develop a reliable, effective approach for GA segmentation in both spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF)(More)
In this paper, we address the problem of both face and body modeling using a single fixed low-cost 3D camera (e.g. Kinect). Unlike other scanning technologies which either set up multiple sensors around a static subject or scan the static subject with a hand-held moving 3D camera, our method allows the subject to move in front of a single fixed 3D sensor.(More)
We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D augmentation technique. The performance of 2D face recognition algorithms has significantly increased by lever-aging the representational power of deep neural networks and the use of large-scale labeled training data. As opposed to 2D face recognition,(More)
Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal(More)
We propose a framework to perform multimodal registration of multiple images. In retinal imaging, this alignment enables the physician to correlate the features across modalities, which can help formulate a diagnosis. The images appear very different and there are few reliable modality-invariant features. We base our registration on the salient line(More)
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