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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1
Efficient Computation of Adaptive Threshold Surfaces for Image Binarization
Kauai, Hawaii
December 08-December 14
ISBN: 0-7695-1272-0
| ASCII Text | x | ||
| Ilya Blayvas, Alfred Bruckstein, Ron Kimmel, "Efficient Computation of Adaptive Threshold Surfaces for Image Binarization," 2012 IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 737, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001. | |||
| BibTex | x | ||
| @article{ 10.1109/CVPR.2001.990549, author = {Ilya Blayvas and Alfred Bruckstein and Ron Kimmel}, title = {Efficient Computation of Adaptive Threshold Surfaces for Image Binarization}, journal ={2012 IEEE Conference on Computer Vision and Pattern Recognition}, volume = {1}, year = {2001}, issn = {1063-6919}, pages = {737}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990549}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE Conference on Computer Vision and Pattern Recognition TI - Efficient Computation of Adaptive Threshold Surfaces for Image Binarization SN - 1063-6919 SP EP A1 - Ilya Blayvas, A1 - Alfred Bruckstein, A1 - Ron Kimmel, PY - 2001 KW - null VL - 1 JA - 2012 IEEE Conference on Computer Vision and Pattern Recognition ER - | |||
The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein proposed to use for image binarization an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and there the image values are between the object and the background gray levels. The threshold surface was determined by successive over-relaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.
Citation:
Ilya Blayvas, Alfred Bruckstein, Ron Kimmel, "Efficient Computation of Adaptive Threshold Surfaces for Image Binarization," cvpr, vol. 1, pp.737, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001
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