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We address the problem of geometric and semantic consistent video segmentation for outdoor scenes. With no assumption on camera movement, we jointly model the semantic-geometric class of spatio-temporal regions (supervoxels) and geometric scene layout in each frame. Our main contribution is to propose a stage scene model to efficiently capture the dependency between the semantic and geometric labels...
We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of multi-instance segmentation using a small set of annotated reference images. We design a novel CRF model that jointly models object appearance, shape deformation, and object...
We address the problem of localizing glass objects with a multimodal RGB-D camera. Our method integrates the intensity and depth information from a single view point, and builds a Markov Random Field that predicts glass boundary and region jointly. Based on the localization, we also reconstruct the depth of the scene and fill in the missing depth values. The efficacy of our algorithm is validated...
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn from a semantic hierarchy, and can also readily cope with missing labels, and roughly-specified object boundaries. We introduce a new form of latent topic model, learning a novel context representation in the joint label-and-image...
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