Paper
29 February 2008 Estimating the original drawing trace of painted strokes
Martin Lettner, Robert Sablatnig
Author Affiliations +
Proceedings Volume 6810, Computer Image Analysis in the Study of Art; 68100C (2008) https://doi.org/10.1117/12.766220
Event: Electronic Imaging, 2008, San Jose, California, United States
Abstract
Pencil drawings like portraits or landscapes comprise dozens of strokes. The segmentation and identification of individual strokes is an interesting question in analyzing the drawings since it allows art historians to analyze the development of the stroke formations in the picture in more detail. In this study we are going to identify individual strokes in stroke formations and to reconstruct the original drawing trace of the artist. The method is based on a thinning algorithm and a following analysis of the accrued skeleton. In order to detect the original stroke and the natural drawing trace we use the curvilinearity information of the thinned sub-strokes. A sub-stroke runs from either a real end point to a crossing point, or between two crossing points. The selection of corresponding strokes in crossing points is based on the angle at the end points of the sub-strokes. The individual strokes drawn through are represented by a one pixel wide line which approximates the original drawing trace of the artist by a cubic B-spline. The whole process is parameter free: we use the automatic calculated stroke width for the skeleton pruning process, for the calculation of the angles at the sub-stroke endings and as the distance for the spline control points.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Martin Lettner and Robert Sablatnig "Estimating the original drawing trace of painted strokes", Proc. SPIE 6810, Computer Image Analysis in the Study of Art, 68100C (29 February 2008); https://doi.org/10.1117/12.766220
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Cited by 4 scholarly publications.
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KEYWORDS
Binary data

Image segmentation

Detection and tracking algorithms

Image processing

Image analysis

Image enhancement

Document image analysis

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