Efficient computation of adaptive threshold surfaces for image binarization
Efficient computation of adaptive threshold surfaces for image binarization
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DOI：
10.1016/j.patcog.2005.08.011
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发表时间：
2006-01-01
影响因子：
8
通讯作者：
Kimmel, R
中科院分区：
文献类型：
--
作者：
Blayvas, I;Bruckstein, A;Kimmel, R
重新考虑在非均匀照明下获取的灰度图像的二值化问题。 Yanowitz 和 Bruckstein 提议使用自适应阈值表面进行图像二值化，该阈值表面是通过在图像梯度高的点处对图像灰度级进行插值来确定的。其基本原理是，高图像梯度表示可能的对象边缘，并且图像值介于对象和背景灰度级之间。通过连续的过度松弛作为拉普拉斯方程的解来确定阈值表面。这项工作提出了一种不同的方法来确定自适应阈值表面。在这种受多分辨率近似启发的新方法中，阈值表面的构建具有相当低的计算复杂性并且是平滑的，从而产生更快的图像二值化和通常更好的噪声鲁棒性。 (c) 2005 年模式识别协会。由爱思唯尔有限公司出版。保留所有权利。
The problem of binarization of gray level images, acquired under non-uniform 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 often better noise robustness. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.