Vectorization in graphics recognition: to thin or not to thin

Vectorization, i.e. raster-to-vector conversion, is a central part of graphics recognition problems. We discuss the pros and the cons of basing one's vectorization process on skeletonization. While distance skeletons have proven to be robust and precise, they tend to distort the results at line extremities and junctions. In these cases, contour-matching approaches yield better results, but they have their own specific problems. A perspective is probably to combine the best of both methods.

[1]  Salvatore Tabbone,et al.  Décomposition de graphiques sous forme de primitives 2D , 2000 .

[2]  Suzanne Collin,et al.  Analysis of Technical Documents: The REDRAW System , 1992 .

[3]  Gabriella Sanniti di Baja Well-Shaped, Stable, and Reversible Skeletons from the (3, 4)-Distance Transform , 1994, J. Vis. Commun. Image Represent..

[4]  Wayne Niblack,et al.  Generating skeletons and centerlines from the distance transform , 1992, CVGIP Graph. Model. Image Process..

[5]  Osamu Hori,et al.  Raster-to-vector conversion by line fitting based on contours and skeletons , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[6]  Rangachar Kasturi,et al.  A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Karl Tombre,et al.  Graphics Recognition Methods and Applications , 1995, Lecture Notes in Computer Science.

[8]  Chin-Chuan Han,et al.  Skeleton generation of engineering drawings via contour matching , 1994, Pattern Recognit..

[9]  Song-Chun Zhu,et al.  Stochastic Jump-Diffusion Process for Computing Medial Axes in Markov Random Fields , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  John Y. Chiang,et al.  A New Algorithm For Line Image Vectorization , 1998, Pattern Recognit..

[11]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[12]  KUO-CHIN FAN,et al.  Skeletonization of binary images with nonuniform width via block decomposition and contour vector matching , 1998, Pattern Recognit..

[13]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Christian Ah-Soon,et al.  Stable, robust and off-the-shelf methods for graphics recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[15]  Albert M. Vossepoel,et al.  Adaptive Vectorization of Line Drawing Images , 1997, Comput. Vis. Image Underst..

[16]  Geoff A. W. West,et al.  Segmentation of edges into lines and arcs , 1989, Image Vis. Comput..

[17]  Osamu Hori,et al.  High quality vectorization based on a generic object model , 1995 .

[18]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Salvatore Tabbone,et al.  Stable and Robust Vectorization: How to Make the Right Choices , 1999, GREC.

[20]  Theodosios Pavlidis,et al.  Feature Analysis Using Line Sweep Thinning Algorithm , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Yuan Chen,et al.  Perfecting Vectorized Mechanical Drawings , 1996, Comput. Vis. Image Underst..

[22]  Karl Tombre,et al.  Improving arc detection in graphics recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[23]  Gladys Monagan,et al.  Adding Geometric Constraints to the Vectorization of Line Drawings , 1995, GREC.

[24]  Vishal Misra,et al.  Detection of Horizontal Lines in Noisy Run Length Encoded Images: The FAST Method , 1995, GREC.