Mean-Shift segmentation and PDE-based nonlinear diffusion: toward a common variational framework for foreground/background document image segmentation

Fadoua Drira; Frank Lebourgeois

doi:10.1007/s10032-017-0285-7

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Mean-Shift segmentation and PDE-based nonlinear diffusion: toward a common variational framework for foreground/background document image segmentation

Fadoua Drira Frank Lebourgeois

Year: 2017 Journal: International Journal on Document Analysis and Recognition (IJDAR) Vol: 20 (3)Pages: 201-222 Publisher: Springer Science+Business Media

DOI: 10.1007/s10032-017-0285-7

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Keywords:

Segmentation Image segmentation Artificial intelligence Scale-space segmentation Image (mathematics) Nonlinear system Diffusion Computer vision Pattern recognition (psychology) Segmentation-based object categorization Mean-shift Computer science Mathematics Physics

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Medical Image Segmentation Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Generative Adversarial Networks and Image Synthesis

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Mean-Shift segmentation and PDE-based nonlinear diffusion: toward a common variational framework for foreground/background document image segmentation

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