On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Steerable-scalable kernels for edge detection and junction analysis
Image and Vision Computing - Special issue: 2nd European Conference on Computer Vision
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Steerable filters and local analysis of image structure
Steerable filters and local analysis of image structure
A computational approach for corner and vertex detection
International Journal of Computer Vision
Computer Vision and Image Understanding
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Affine Morphological Multiscale Analysis of Corners andMultiple Junctions
International Journal of Computer Vision
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of General Edges and Keypoints
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Visual Organization for Figure/Ground Separation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Polymorphic grouping for image segmentation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Computer matching of areas in stereo images.
Computer matching of areas in stereo images.
Good continuations in digital image level lines
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Detection and characterization of junctions in a 2D image
Computer Vision and Image Understanding
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An Axiomatic Approach to Corner Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Efficient Non-Maximum Suppression
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Primal sketch: Integrating structure and texture
Computer Vision and Image Understanding
A model-based approach to junction detection using radial energy
Pattern Recognition
Decomposition of a visual scene into three-dimensional bodies
AFIPS '68 (Fall, part I) Proceedings of the December 9-11, 1968, fall joint computer conference, part I
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Exploiting T-junctions for depth segregation in single images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Angle Detection on Digital Curves
IEEE Transactions on Computers
A Statistical Approach to the Matching of Local Features
SIAM Journal on Imaging Sciences
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Images with Local Features
International Journal of Computer Vision
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Non-maximum suppression of gradient magnitudes makes them easier to threshold
Pattern Recognition Letters
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
Resolution- Independent Characteristic Scale Dedicated to Satellite Images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Accurate junction detection and characterization are of primary importance for several aspects of scene analysis, including depth recovery and motion analysis. In this work, we introduce a generic junction analysis scheme. The first asset of the proposed procedure is an automatic criterion for the detection of junctions, permitting to deal with textured parts in which no detection is expected. Second, the method yields a characterization of L-, Y- and X- junctions, including a precise computation of their type, localization and scale. Contrary to classical approaches, scale characterization does not rely on the linear scale-space. First, an a contrario approach is used to compute the meaningfulness of a junction. This approach relies on a statistical modeling of suitably normalized gray level gradients. Then, exclusion principles between junctions permit their precise characterization. We give implementation details for this procedure and evaluate its efficiency through various experiments.