Thomas Breuel

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This thesis addresses the problem of visual recognition under two sources of variability: geometric and photometric. The geometric deals with the relation between 3D objects and their views under parallel, perspective, and central projection. The photometric deals with the relation between 3D matte objects and their images under changing illumination(More)
In this dissertation I address the problem of visual recognition of non-rigid objects. I introduce the frame alignment approach to recognition and illustrate it in two types of non-rigid objects: contour textures and elongated exible objects. Frame alignment is based on matching stored models to images and has three stages: rst, a \frame curve" and a(More)
State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant con-textual dependence. In this work, we advocate the use of spatially recurrent(More)
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