Literature DB >> 16724584

Robust and accurate vectorization of line drawings.

Xavier Hilaire1, Karl Tombre.   

Abstract

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

Mesh:

Year:  2006        PMID: 16724584     DOI: 10.1109/TPAMI.2006.127

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  2 in total

1.  Cleanup Sketched Drawings: Deep Learning-Based Model.

Authors:  Amal Ahmed Hasan Mohammed; Jiazhou Chen
Journal:  Appl Bionics Biomech       Date:  2022-05-06       Impact factor: 1.664

2.  Automatic structural scene digitalization.

Authors:  Rui Tang; Yuhan Wang; Darren Cosker; Wenbin Li
Journal:  PLoS One       Date:  2017-11-17       Impact factor: 3.240

  2 in total

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