Jean-Claude Paul

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We present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is(More)
We present a novel framework based on a continuous fluid simulator for general simulation of realistic bubbles, with which we can handle as many significant dynamic bubble effects as possible. To capture nature of the very thin liquid film of bubbles, we have developed a regional level set method allowing multi-manifold interface tracking. The regional(More)
This paper presents a novel content-based method for transferring the colour patterns between images. Unlike previous methods that rely on image colour statistics, our method puts an emphasis on high-level scene content analysis. We first automatically extract the foreground subject areas and background scene layout from the scene. The semantic(More)
We present a novel hierarchical grid based method for fast collision detection (CD) for deformable models on GPU architecture. A two-level grid is employed to accommodate the non-uniform distribution of practical scene geometry. A bottom-to-top method is implemented to assign the triangles into the hierarchical grid without any iteration while a deferred(More)
Current multi-operator image resizing methods succeed in generating impressive results by using image similarity measure to guide the resizing process. An optimal operation path is found in the resizing space. However, their slow resizing speed caused by inefficient computation strategy of the bidirectional patch matching becomes a drawback in practical(More)
Color transfer is an image editing technique which arises various applications, from daily photo appearance enhancement to movie post-processing. An ideal color transfer algorithm should keep the scene from the source image and apply the color style of the target image. All the dominant colors in the target image should be transferred to the source, while(More)
Manifold-ranking is a powerful method in semi-supervised learning, and its performance heavily depends on the quality of the constructed graph. In this paper, we propose a novel graph structure named k-regular nearest neighbor (k-RNN) graph as well as its constructing algorithm, and apply the new graph structure in the framework of manifold-ranking based(More)
Point cloud is a basic description of discrete shape information. Parameterization of unorganized points is important for shape analysis and shape reconstruction of natural objects. In this paper we present a new algorithm for global parameterization of an unorganized point cloud and its application to the meshing of the cloud. Our method is guided by(More)