基于视觉注意的动态阈值分割算法

Dynamic threshold segmentation based on visual attention

提出一种基于视觉注意机制的动态阈值选取方法.首先按人眼视觉注意特点计算各图块的显著性特征值,根据显著性特征值将图像块进行分类,然后利用最大类间方差法和多尺度的自适应阈值方法分别实现不同灰度特性区域的图像动态分割.实验结果表明:该方法能够实现图像多阈值的自动选取,不受照明条件的影响,能够获得满意的二值分割效果.

A dynamic self-adaptive threshold segmentation algorithm based on human visual attention theory and maximum between-class variance method is proposed. First of all, the pending image is divided into lots of equal- sized square blocks, then the feature value of every block is calculated respectively according to multi-scale visual attention. Secondly, all the image blocks are divided into two types by classification algorithm based on feature value of visual attention. Finally, blocks with different types are binarized by maximum variance algorithm and multi-scale adaptive threshold algorithm respectively. The experimental results show that：compared with the one dimension max- imum variance and two dimension maximum variance segmentation methods, this new proposed method can elimi- nate the disturbance of the uneven lighting and background noises, thus achieve superior segmented results for une- ven lighting and poor quality document image binarization. These experimental results testify the validity and practi- cal value of the proposed method.