Title :
Automated stroke ending analysis for drawing tool classification
Author :
Vill, Maria C. ; Sablatnig, Robert
Author_Institution :
Pattern Recognition & Image Process. Group, Vienna Univ. of Technol., Vienna, Austria
Abstract :
This paper proposes a drawing tool recognition method based on features calculated from the shape of stroke endings. The application for this method is to help art historians to identify the drawing tool used for a drawing. Since the style of a drawing depends on the drawing tool used, drawing tool recognition is an important step toward a style analysis. A dominant feature of a drawn stroke is its ending. Several features regarding curvature, proportions etc. are calculated out of the shape of the endings. These features are then used to classify stroke endings with a SVM classifier.
Keywords :
art; image classification; image segmentation; image texture; object recognition; support vector machines; SVM classifier; automated stroke ending analysis; drawing tool classification; drawing tool recognition; historian art; image classification; image segmentation; stroke texture analysis; Art; Gray-scale; Image analysis; Image segmentation; Joining processes; Painting; Pattern analysis; Pattern recognition; Shape; Skeleton;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761171