Image sequence stabilization in real time
Real-Time Imaging
Vanishing Point Detection by Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Camera Calibration from a Single Manhattan Image
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Vanishing Point Detection without Any A Priori Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manhattan World: Compass Direction from a Single Image by Bayesian Inference
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Cascaded Hough Transform as an Aid in Aerial Image Interpretation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Putting Objects in Perspective
International Journal of Computer Vision
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Interpreting perspective images
Artificial Intelligence
Geometric Image Parsing in Man-Made Environments
International Journal of Computer Vision
Real-time estimation of 3D scene geometry from a single image
Pattern Recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
People watching: human actions as a cue for single view geometry
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Ultra-wide baseline facade matching for geo-localization
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
An accurate method for line detection and manhattan frame estimation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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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 spanning different layers from low level (edges) through mid-level (lines and vanishing points) to high level (the zenith and the horizon). The inference in such a model thus jointly and simultaneously estimates a) the grouping of edges into the straight lines, b) the grouping of lines into parallel families, and c) the positioning of the horizon and the zenith in the image. Such a unified treatment means that the uncertainty information propagates between the layers of the model. This is in contrast to most previous approaches to the same problem, which either ignore the middle levels (lines) all together, or use the bottom-up step-by-step pipeline. For the evaluation, we consider a publicly available York Urban dataset of "Manhattan" scenes, and also introduce a new, harder dataset of 103 urban outdoor images containing many non-Manhattan scenes. The comparative evaluation for the horizon estimation task demonstrate higher accuracy and robustness attained by our method when compared to the current state-of-the-art approaches.