Abstract :
This paper demonstrates how the nonlocal principle benefits video matting via the KNN Laplacian, which comes with a straightforward implementation using motion-aware K nearest neighbors. In hindsight, the fundamental problem to solve in video matting is to produce spatio-temporally coherent clusters of moving foreground pixels. When used as described, the motion-aware KNN Laplacian is effective in addressing this fundamental problem, as demonstrated by sparse user markups typically on only one frame in a variety of challenging examples featuring ambiguous foreground and background colors, changing topologies with disocclusion, significant illumination changes, fast motion, and motion blur. When working with existing Laplacian-based systems, we expect our Laplacian can benefit them immediately with an improved clustering of moving foreground pixels.
Keywords :
pattern clustering; video signal processing; background color; changing topologies; fast motion; foreground color; illumination change; motion aware K nearest neighbor; motion aware KNN Laplacian; motion blur; moving foreground pixels; nonlocal principle; sparse user markup; spatio temporally coherent cluster; video matting; Image color analysis; Integrated optics; Laplace equations; Noise reduction; Optical imaging; Optical scattering; Vectors;