Selective Extraction of Entangled Textures via Adaptive PDE Transform | Zendy

Yang Wang | Zendy; GuoWei Wei | Zendy; Siyang Yang | Zendy

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Selective Extraction of Entangled Textures via Adaptive PDE Transform

Author(s) -

Yang Wang,

GuoWei Wei,

Siyang Yang

Publication year - 2012

Publication title -

international journal of biomedical imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.626

H-Index - 41

eISSN - 1687-4196

pISSN - 1687-4188

DOI - 10.1155/2012/958142

Subject(s) - computer science , quantum entanglement , mode (computer interface) , pattern recognition (psychology) , variance (accounting) , feature extraction , extraction (chemistry) , decomposition , partial differential equation , orientation (vector space) , texture (cosmology) , artificial intelligence , image (mathematics) , mathematics , physics , ecology , mathematical analysis , chemistry , geometry , accounting , chromatography , quantum mechanics , business , quantum , biology , operating system

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.

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