Pattern recognition and image analysis
Pattern recognition and image analysis
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Photorealistic Rendering
Digital Image Processing
Stylization and abstraction of photographs
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ACM SIGGRAPH 2004 Papers
Keyframe-based tracking for rotoscoping and animation
ACM SIGGRAPH 2004 Papers
Fast median and bilateral filtering
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
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Non-Photorealistic Rendering (NPR) can offer increased concentration and familiarity to the watcher. For this reason, many media such as movies, games, and commercials currently use the NPR method to deliver information. In this paper, we suggest an interactive system for video cartooning based on the mean-shift segmentation of image and video. In order to solve the problems of time complexity and memory allocation, the conventional problems of video mean-shift segmentation, this paper proposes several techniques such as foreground object based segmentation and sequential segmentation. We also propose the interactive correction technique to get enhanced results. For more cartoonic representation, we used spline curve approximation of segment boundaries in the final rendering results. With our method, we can easily create cartoon rendering output using the video streams such like home video, which can be obtained easily in our daily life.