A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An on-line algorithm for fitting straight lines between data ranges
Communications of the ACM
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast, accurate and convergent tangent estimation on digital contours
Image and Vision Computing
Optimal blurred segments decomposition of noisy shapes in linear time
Computers and Graphics
Multi-scale Analysis of Discrete Contours for Unsupervised Noise Detection
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Adaptive Pixel Resizing for Multiscale Recognition and Reconstruction
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Discrete analytical curve reconstruction without patches
Image and Vision Computing
Fast distance transformation on irregular two-dimensional grids
Pattern Recognition
Linear Decomposition of Planar Shapes
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Topological and geometrical reconstruction of complex objects on irregular isothetic grids
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
Computer Vision and Image Understanding
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
| Hi-index | 0.00 |
In this paper, we present an original algorithm to build a polygonal reconstruction of noisy digital contours. For this purpose, we first improve an algorithm devoted to the vectorization of discrete irregular isothetic objects. Afterwards we propose to use it to define a reconstruction process of noisy digital contours. More precisely, we use a local noise detector, introduced by Kerautret and Lachaud in IWCIA 2009, that builds a multi-scale representation of the digital contour, which is composed of pixels of various size depending of the local amount of noise. Finally, we compare our approach with previous works, by considering the Hausdorff distance and the error on tangent orientations of the computed line segments to the original perfect contour. Thanks to both synthetic and real noisy objects, we show that our approach has interesting performance, and could be integrated into document analysis systems.