A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic approach to the Hough transform
Image and Vision Computing
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
Finding Picture Edges Through Collinearity of Feature Points
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
Interpreting perspective images
Artificial Intelligence
Geometric image parsing in man-made environments
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
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We address the problem of estimating the rotation of a camera relative to the canonical frame of an urban scene, from a single image. Solutions generally rely on the so-called 'Manhattan World' assumption [1] that the major structures in the scene conform to three orthogonal principal directions. This can be expressed as a generative model in which the dense gradient map of the image is explained by a mixture of the three principal directions and a background process [2]. It has recently been shown that using sparse oriented edges rather than the dense gradient map leads to substantial gains in both accuracy and speed [3]. Here we explore whether further gains can be made by basing inference on even sparser extended lines. Standard Houghing techniques suffer from quantization errors and noise that make line extraction unreliable. Here we introduce a probabilistic line extraction technique that eliminates these problems through two innovations. First, we accurately propagate edge uncertainty from the image to the Hough map through a bivariate normal kernel that uses natural image statistics, resulting in a non-stationary 'soft-voting' technique. Second, we eliminate multiple responses to the same line by updating the Hough map dynamically as each line is extracted. We evaluate the method on a standard benchmark dataset [3], showing that the resulting line representation supports reliable estimation of the Manhattan frame, bettering the accuracy of previous edge-based methods by a factor of 2 and the gradient-based Manhattan World method by a factor of 5.