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Chapter and Conference Paper
IterDet: Iterative Scheme for Object Detection in Crowded Environments
Deep learning-based detectors tend to produce duplicate detections of the same objects. After that, the detections are filtered via a non-maximum suppression algorithm (NMS) so that there remains only one boun...
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Chapter and Conference Paper
Learning High-Resolution Domain-Specific Representations with a GAN Generator
In recent years generative models of visual data have made a great progress, and now they are able to produce images of high quality and diversity. In this work we study representations learnt by a GAN generat...
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Article
Fast and accurate scene text understanding with image binarization and off-the-shelf OCR
While modern off-the-shelf OCR engines show particularly high accuracy on scanned text, text detection and recognition in natural images still remain a challenging problem. Here, we demonstrate that OCR engine...
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Chapter and Conference Paper
Learning Graph Laplacian for Image Segmentation
In this paper we formulate the task of semantic image segmentation as a manifold embedding problem and solve it using graph Laplacian approximation. This allows for unsupervised learning of graph Laplacian par...
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Article
Geometric Image Parsing in Man-Made Environments
We present a new optimization based parsing framework for the geometric analysis of a single image coming from a man-made environment. This framework models the scene as a composition of geometric primitives s...
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Chapter and Conference Paper
Large-Lexicon Attribute-Consistent Text Recognition in Natural Images
This paper proposes a new model for the task of word recognition in natural images that simultaneously models visual and lexicon consistency of words in a single probabilistic model. Our approach combines loca...
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Chapter and Conference Paper
Geometric Image Parsing in Man-Made Environments
We present a new parsing framework for the line-based geometric analysis of a single image coming from a man-made environment. This parsing framework models the scene as a composition of geometric primitives s...
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Chapter and Conference Paper
ODDboost: Incorporating Posterior Estimates into AdaBoost
Boosting methods while being among the best classification methods developed so far, are known to degrade performance in case of noisy data and overlap** classes. In this paper we propose a new upper general...
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Chapter and Conference Paper
Fast Automatic Single-View 3-d Reconstruction of Urban Scenes
We consider the problem of estimating 3-d structure from a single still image of an outdoor urban scene. Our goal is to efficiently create 3-d models which are visually pleasant. We chose an appropriate 3-d mo...
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Chapter and Conference Paper
Avoiding Boosting Overfitting by Removing Confusing Samples
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorithms are not appropriate in ca...
