Algorithms for clustering data
Algorithms for clustering data
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Detection of linear objects in GPR data
Signal Processing
Detection of linear and planar structures in 3D subsurface images by iterative dimension reduction
Digital Signal Processing
Detection of planar regions in volume data for topology optimization
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Triangulation-Based plane extraction for 3d point clouds
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
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In this article we propose an algorithmic approach - the detection and the characterization of planar fractures based on the analysis of 3D data relative to rock samples (coming from X-ray/NMR tomography). Data analysis is based on a particular implementation of the Hough Transform for the detection of planes in a 3D space. One of the original aspects of our approach is the pattern detection strategy. In fact, it works in an iterative fashion and consists of the progressive removal of the layers that constitute the cumulative array in the Hough space. At each step, we first determine the leading fracture from the analysis of the Hough cumulative arrays, and then we remove the corresponding cumulative layer in order to enable the detection of the next important fracture. We also show the results of some tests, which prove the proposed method effective even with very complex data sets.