Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Interval arithmetic: From principles to implementation
Journal of the ACM (JACM)
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Reliable and Efficient Pattern Matching Using an Affine Invariant Metric
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended Summary of the Arc Segmentation Contest
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
RANVEC and the Arc Segmentation Contest
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
TIF2VEC, An Algorithm for Arc Segmentation in Engineering Drawings
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
On the use of interval arithmetic in geometric branch and bound algorithms
Pattern Recognition Letters
Representations and Metrics for Off-Line Handwriting Segmentation
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
The third report of the arc segmentation contest
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
A circle-based vectorization algorithm for drawings with shadows
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
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The robust detection of lines and arcs in scanned documents or technical drawings is an important problem in document image understanding. We present a new solution to this problem that works directly on run-length encoded data. The method finds globally optimal solutions to parameterized thick line and arc models. Line thickness is part of the model and directly used during the matching process. Unlike previous approaches, it does not require any thinning or other preprocessing steps, no computation of the line adjacency graphs, and no heuristics. Furthermore, the only search-related parameter that needs to be specified is the desired numerical accuracy of the solution. The method is based on a branch-and-bound approach for the globally optimal detection of these geometric primitives using runs of black pixels in a bi-level image. We present qualitative and quantitative results of the algorithm on images used in the 2003 and 2005 GREC arc segmentation contests.