Open Access Open Access  Restricted Access Subscription Access

Resolution Independent 2D Cartoon Video Conversion


Affiliations
1 University of Moratuwa, Sri Lanka
 

This paper describes a novel system for vectorizing 2D raster cartoon. The output videos are the resolution independent, smaller in file size. As a first step, input video is segment to scene thereafter all processes are done for each scene separately. Every scene contains foreground and background objects so in each and every scene foreground background classification is performed. Background details can occlude by foreground objects but when foreground objects move its previous position such occluded details exposed in one of the next frame so using that frame can fill the occluded area and can generate static background. Classified foreground objects are identified and the motion of the foreground objects tracked for this simple user assistance is required from those motion details of foreground object's animation generated. Static background and foreground objects segmented using K-means clustering and each and every cluster's vectorized using potrace. Using vectored background and foreground object animation path vector video regenerated.

Keywords

Vectorizing, foreground Extraction and Identification, Background Extraction, Background Filling, Motion Tracking, Scene Segmentation, Clustering.
User
Notifications
Font Size

Abstract Views: 163

PDF Views: 2




  • Resolution Independent 2D Cartoon Video Conversion

Abstract Views: 163  |  PDF Views: 2

Authors

M. S. F. Fayaza
University of Moratuwa, Sri Lanka
M. A. M. Aazeer
University of Moratuwa, Sri Lanka
M. F. H. M. Adheeb
University of Moratuwa, Sri Lanka
K. F. Muhiminah
University of Moratuwa, Sri Lanka
S. C. Premarathna
University of Moratuwa, Sri Lanka

Abstract


This paper describes a novel system for vectorizing 2D raster cartoon. The output videos are the resolution independent, smaller in file size. As a first step, input video is segment to scene thereafter all processes are done for each scene separately. Every scene contains foreground and background objects so in each and every scene foreground background classification is performed. Background details can occlude by foreground objects but when foreground objects move its previous position such occluded details exposed in one of the next frame so using that frame can fill the occluded area and can generate static background. Classified foreground objects are identified and the motion of the foreground objects tracked for this simple user assistance is required from those motion details of foreground object's animation generated. Static background and foreground objects segmented using K-means clustering and each and every cluster's vectorized using potrace. Using vectored background and foreground object animation path vector video regenerated.

Keywords


Vectorizing, foreground Extraction and Identification, Background Extraction, Background Filling, Motion Tracking, Scene Segmentation, Clustering.