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In various computer vision applications, often we need to convert an image in one style into another style for better visualization, interpretation and recognition; for examples , up-convert a low resolution image to a high resolution one, and convert a face sketch into a photo for matching, etc. A semi-coupled dictionary learning (SCDL) model is proposed(More)
Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representation and classification, in this paper we present a novel(More)
In this paper we are interested in exploiting geographic priors to help outdoor scene understanding. Towards this goal we propose a holistic approach that reasons jointly about 3D object detection, pose estimation, semantic seg-mentation as well as depth reconstruction from a single image. Our approach takes advantage of large-scale crowd-sourced maps to(More)
In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and lo-calization tasks. In this paper we propose to exploit aerial images in order to enhance freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field(More)
In this paper, we prove that every multivariate polynomial with even degree can be decomposed into a sum of convex and concave polynomials. Motivated by this property, we exploit the concave-convex procedure to perform inference on continuous Markov random fields with polynomial potentials. In particular, we show that the concave-convex decomposition of(More)
In this paper we propose a novel approach to localiza-tion in very large indoor spaces (i.e., shopping malls with over 200 stores) that takes a single image and a floor plan of the environment as input. We formulate the localiza-tion problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop names in the image(More)
This paper proposes a novel extremely efficient, fully-parallelizable, task-specific algorithm for the computation of global point-wise correspondences in images and videos. Our algorithm, the Global Patch Collider, is based on detecting unique collisions between image points using a collection of learned tree structures that act as conditional hash(More)
In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available carto-graphic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection,(More)