We propose a trapped-ball method for image segmentation, which is fast, supports non-uniformly colored regions, and allows robust region segmentation even in the presence of imperfectly linked region edges. We also introduce two applications by using the trapped-ball image segmentation, for temporal coherent animations generation and editing. First, we present a system for vectorizing 2D raster format cartoon animations by the segmentation method and background construction. The output animations are visually flicker free, smaller in file size, and easy to edit. And then we present an automatic method for online video stream stylization, producing a temporally coherent output video stream, based on the trapped-ball segmentation and optical flow. Our system transforms video into an abstract style with large regions of constant color and highlighted bold edges.

This paper presents a novel algorithm for synthesizing animations of running water, such as waterfalls and rivers, in the style of Chinese paintings, for applications such as cartoon making. All video frames are first registered in a common coordinate system, simultaneously segmenting the water from background and computing optical flow of the water. Taking artists' advice into account, we produce a painting structure to guide painting of brush strokes. Flow lines are placed in the water following an analysis of variance of optical flow, to cause strokes to be drawn where the water is flowing smoothly, rather than in turbulent areas: this allows a few moving strokes to depict the trends of the water flows. A variety of brush strokes is then drawn using a template determined from real Chinese paintings. The novel contributions of this paper are: a method for painting structure generation for flows in videos, and a method for stroke placement, with the necessary temporal coherence

We present a system for vectorizing 2D raster format cartoon animations. The output animations are visually flicker free, smaller in file size, and easy to edit. We identify decorative lines separately from colored regions. We use an accurate and semantically meaningful image decomposition algorithm, supporting an arbitrary color model for each region. To ensure temporal coherence in the output, we reconstruct a universal background for all frames and separately extract foreground regions. Simple user-assistance is required to complete the background. Each region and decorative line is vectorized and stored together with their motions from frame to frame. The contributions of this paper are: 1) the new trapped-ball segmentation method, which is fast, supports nonuniformly colored regions, and allows robust region segmentation even in the presence of imperfectly linked region edges, 2) the separate handling of decorative lines as special objects during image decomposition, avoiding results containing multiple short, thin oversegmented regions, and 3) extraction of a single patch-based background for all frames, which provides a basis for consistent, flicker-free animations.

A novel method is given for content‐aware video resizing, i.e. targeting video to a new resolution (which may involve aspect ratio change) from the original.

Video structure analysis is a basic requirement for most content-based video editing and process- ing systems. This paper presents a fast video structure analysis method based on image segmentation in each frame, with region matching between frames. The structure analysis decomposes the video into sev- eral moving objects, including information about their colors, positions, shapes, movements, and lifetimes. The method also supports user interactions to improve the results. The result shows that this method is fast and stable and can complete video analyzing interactively.