A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precision Edge Contrast and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using vanishing points for camera calibration
International Journal of Computer Vision
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust detection of lines using the progressive probabilistic Hough transform
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Paracatadioptric Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Method for Geometric Feature Matching and Model Extraction
International Journal of Computer Vision
Deformable Kernels for Early Vision
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
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Geometric Properties of Central Catadioptric Line Images and Their Application in Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Edge landmarks in monocular SLAM
Image and Vision Computing
The impact of radial distortion on the self-calibration of rotating cameras
Computer Vision and Image Understanding
Equidistant (fθ) fish-eye perspective with application in distortion centre estimation
Image and Vision Computing
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
Machine Vision and Applications
A moment-based unified approach to image feature detection
IEEE Transactions on Image Processing
Globally optimal line clustering and vanishing point estimation in Manhattan world
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Are we making real progress in computer vision today?
Image and Vision Computing
A multi-stage linear approach to structure from motion
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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The estimation of camera orientation from image lines using the anthropic environment restriction is a well-known problem, but traditional methods to solve it depend on line extraction, a relatively complex procedure that is also incompatible with distorted images. We propose Corisco, a monocular orientation estimation method based on edgels instead of lines. Edgels are points sampled from image edges with their tangential directions, extracted in Corisco using a grid mask. The estimation aligns the measured edgel directions with the predicted directions calculated from the orientation, using a known camera model. Corisco uses the M-estimation technique to define an objective function that is optimized by two algorithms in sequence: RANSAC, which gives robustness and flexibility to Corisco, and FilterSQP, which performs a continuous optimization to refine the initial estimate, using closed formulas for the function derivatives. Corisco is the first edgel-based method able to analyze images with any camera model, and it also allows for a compromise between speed and accuracy, so that its performance can be tuned according to the application requirements. Our experiments demonstrate the effectiveness of Corisco with various camera models, and its performance surpasses similar edgel-based methods. The accuracy displayed a mean error below 2^o for execution times above 8s in a conventional computer, and above 3^o for less than 2s.