Computer Aided Geometric Design
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2006 Papers
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Mean value coordinates for arbitrary planar polygons
ACM Transactions on Graphics (TOG)
Efficient gradient-domain compositing using quadtrees
ACM SIGGRAPH 2007 papers
GPU-assisted positive mean value coordinates for mesh deformations
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Streaming multigrid for gradient-domain operations on large images
ACM SIGGRAPH 2008 papers
Coordinates for instant image cloning
ACM SIGGRAPH 2009 papers
Sketch2Photo: internet image montage
ACM SIGGRAPH Asia 2009 papers
Natural and seamless image composition with color control
IEEE Transactions on Image Processing
Content-aware copying and pasting in images
The Visual Computer: International Journal of Computer Graphics
Error-tolerant image compositing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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Image composition usually floods the composition region of a target image with the same shape as a source image patch. To achieve seamless transition effect, the tone of the boundary in the target image is then transferred to the interior region of the source patch. Traditional approaches usually fail to work for the case that the corresponding boundaries of target and source images don't match well because the tone transformation of all pixels on the boundary are equally propagated to the inner region. This paper presents a new image composition technique based on discrete mean value coordinates(DMVC), which supports the transition of tone transformation of part selected not all pixels on the boundary to the inner region. The approach works as follows. It firstly selects boundary pixels having good matching. The new color of inner pixels is then calculated using DMVC according to those selected pixel pairs from the source and target boundaries. Matting technique is finally introduced to compose the new pixels to the target image. Experiments show that the proposed approach can obtain reasonable results for examples with inconsistent boundaries between source and target images.