IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
Symbolic Construction of a 2-D Scale-Space Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical approach to line extraction based on the Hough transform
Computer Vision, Graphics, and Image Processing
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Extracting straight lines by sequential fuzzy clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Collinearity and Weak Collinearity in the Digital Plane
Digital and Image Geometry, Advanced Lectures [based on a winter school held at Dagstuhl Castle, Germany in December 2000]
A New Line Segment Grouping Method for Finding Globally Optimal Line Segments
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Collinearity and weak collinearity in the digital plane
Digital and image geometry
Mean shift based clustering of Hough domain for fast line segment detection
Pattern Recognition Letters
Modelling ECG signals with hidden Markov models
Artificial Intelligence in Medicine
Consensus sets for affine transformation uncertainty polytopes
Computers and Graphics
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Applying an artificial neural network to building reconstruction
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Image feature detection as robust model fitting
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Uncertain geometry in computer vision
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
Technical Section: Reconstructing shape boundaries with multimodal constraints
Computers and Graphics
Short Communication: A rectilinear Gaussian model for estimating straight-line parameters
Journal of Visual Communication and Image Representation
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This correspondence presents a metric for describing line segments. This metric measures how well two line segments can be replaced by a single longer one. This depends for example on collinearity and nearness of the line segments. The metric is constructed using a new technique using so-called neighborhood functions. The behavior of the metric depends on the neighborhood function chosen. In this correspondence, an appropriate choice for the case of line segments is presented. The quality of the metric is verified by using it in a simple clustering algorithm that groups line segments found by an edge detection algorithm in an image. The fact that the clustering algorithm can detect long linear structures in an image shows that the metric is a good measure for the groupability of line segments.