DocumentCode :
865780
Title :
Extraction of line segments and circular arcs from freehand strokes based on segmental homogeneity features
Author :
Zhang, Xiwen ; Song, Jiqiang ; Dai, Guozhong ; Lyu, Michael R.
Author_Institution :
Lab. of Human-Comput. Interaction & Intelligent Inf. Process., Chinese Acad. of Sci., Beijing, China
Volume :
36
Issue :
2
fYear :
2006
fDate :
4/1/2006 12:00:00 AM
Firstpage :
300
Lastpage :
311
Abstract :
The extraction of component line segments and circular arcs from freehand strokes along with their relations is a prerequisite for sketch understanding. Existing approaches usually take three stages to segment a stroke: first identifying segmentation points, then classifying the substroke between each pair of adjacent segmentation points, and, finally, obtaining graphical representations of substrokes by fitting graphical primitives to them. Since a stroke inevitably contains noises, the first stage may produce wrong or inaccurate segmentation points, resulting in the wrong substroke classification in the second stage and inaccurately fitted parameters in the third stage. To overcome the noise sensitivity of the three-stage method, the segmental homogeneity feature is emphasized in this paper. We propose a novel approach, which first extracts graphical primitives from a stroke by a connected segment growing from a seed-segment and then utilizes relationships between the primitives to refine their control parameters. We have conducted experiments using real-life strokes and compared the proposed approach with others. Experimental results demonstrate that the proposed approach is effective and robust.
Keywords :
computational geometry; feature extraction; image classification; image segmentation; circular arc extraction; component line segment extraction; freehand strokes; segmental homogeneity features; segmentation point identification; sketch understanding; stroke recognition; stroke segmentation; substroke classification; Computer science; Councils; Information processing; Information representation; Laboratories; Research and development; Robustness; Sampling methods; Software tools; Segmental homogeneity feature; sketch understanding; stroke recognition; stroke segmentation; Algorithms; Artificial Intelligence; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Paintings; Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2005.857288
Filename :
1605378
Link To Document :
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