A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Line Finder for Vision-Guided Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Features for Recognition: Viewpoint Invariance for Non-Planar Scenes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Computer Description of Curved Objects
IEEE Transactions on Computers
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
Geometric image parsing in man-made environments
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Energy-Based Geometric Multi-model Fitting
International Journal of Computer Vision
Geometric Image Parsing in Man-Made Environments
International Journal of Computer Vision
Manhattan scene understanding using monocular, stereo, and 3D features
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We introduce the self-similar sketch, a new method for the extraction of intermediate image features that combines three principles: detection of self-similarity structures, nonaccidental alignment, and instance-specific modelling. The method searches for self-similar image structures that form nonaccidental patterns, for example collinear arrangements. We demonstrate a simple implementation of this idea where self-similar structures are found by looking for SIFT descriptors that map to the same visual words in image-specific vocabularies. This results in a visual word map which is searched for elongated connected components. Finally, segments are fitted to these connected components, extracting linear image structures beyond the ones that can be captured by conventional edge detectors, as the latter implicitly assume a specific appearance for the edges (steps). The resulting collection of segments constitutes a "sketch" of the image. This is applied to the task of estimating vanishing points, horizon, and zenith in standard benchmark data, obtaining state-of-the-art results. We also propose a new vanishing point estimation algorithm based on recently introduced techniques for the continuous-discrete optimisation of energies arising from model selection priors.