Robust estimation of texture flow via dense feature sampling

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Texture flow estimation is a valuable step in a variety of vision related tasks, including texture analysis, image segmentation, shape-from-texture and texture remapping. This paper describes a novel and effective technique to estimate texture flow in an image given a small example patch. The key idea consists of extracting a dense set of features from the example patch where discrete orientations are encapsulated into the feature vector such that rotation can be simulated as a linear shift of the vector. This dense feature space is then compressed by PCA and clustered using EM to produce a set of small set of principal features. Obtaining these principal features at varying image scales, we can compute the per-pixel scale and orientation likelihoods for the distorted texture. The final texture flow estimation is formulated as the MAP solution of a labeling Markov network which is solved using belief propagation. Experimental results on both synthetic and real images demonstrate good results even for highly distorted examples.
Publisher
IEEE Computer Society
Issue Date
2007-06
Language
ENG
Citation

Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, v.0, no.0, pp.1 - 8

ISSN
1063-6919
URI
http://hdl.handle.net/10203/162353
Appears in Collection
EE-Conference Papers(학술회의논문)
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