Joan Serrat Gual

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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can(More)
The Hierarchical Conditional Random Field (HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple(More)
Object detection is an open research problem in computer vision, and most important recent advances make use of parts-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into the partsbased model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRFbased(More)
Twenty-eight upper extremity nerves in 25 patients were repaired using an electrical nerve stimulator to identify major fascicles for their sensory and motor content. The proximal stump was searched for major sensory fascicles, as determined by the patient's verbal response, while using a general anesthesia "wake-up" technique. The distal stump was searched(More)
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