. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them

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probabilistic model which includes shape potentials for the object to incorporate top-down information that is global across the image, in addition to the grid clique potentials which provide the bottom-up information used in previous approaches. The shape potentials are provided by the pose of the object

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on the possible composition of facades. The algorithm explores the space of feasible solutions by generating the possible configurations of the facade and comparing it to the input data by means of a local, pixelor patch-based classifier. We propose new bottom-up cues for the algorithm, both for evaluation of a

complexity. Secondly, the framework yields flexibility to fuse top down (global cues as hyperedges) and bottom up (local superpixels as nodes) information. Subsequently, a procedure for auto-seeding to initialize the tracing procedure is proposed. The paper concludes with experimental validation on a

probabilistic model which includes shape potentials for the object to incorporate top-down information that is global across the image, in addition to the grid clique potentials which provide the bottom-up information used in previous approaches. The shape potentials are provided by the pose of the object

Abstract. Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-specific image information. Despite the success of top-down algorithms, they often give coarse segmentations

image cues. Bottom--up cues are detected by decomposing the image into a number of feature and conspicuity maps, while a--priori knowledge (i.e. models) about objects is used to generate top--down attention cues. Bottom--up and top-- down information is combined through a non--linear relaxation process

multiple image cues. Bottom--up cues are detected by decomposing the image into a number of feature and conspicuity maps, while a--priori knowledge (i.e. models) about objects is used to generate top--down attention cues. Bottom--up and top--down information is combined through a non--linear relaxation