Selective extraction of entangled textures via adaptive PDE transform

Authors:
Yang Wang;Guo-Wei Wei;Siyang Yang
Affiliations:
Department of Mathematics, Michigan State University, East Lansing, MI;Department of Mathematics, Michigan State University, East Lansing, MI and Department of Electrical and Computer Engineering;Department of Mathematics, Michigan State University, East Lansing, MI
Venue:
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
Year:
2012

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Abstract

Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.