基于SUSAN边缘信息的阈值分割算法

基于边缘信息的阈值分割方法因为在保持目标轮廓和分割低对比度图像方面具有良好性能,特别适用于对工业生产图片的分割,但是传统方法普遍存在对噪声敏感和阈值难以选取的问题,针对这些问题,提出一种基于SUSAN边缘信息的自适应图像阈值分割算法,使用SUSAN特征响应描述像素的边缘信息,以有效抑制噪声和弱边界的影响。基于图谱理论的最小最大割阈值分割算法相比于其他分割算法时空复杂度大大降低,且获取的阈值全局最优。实验结果表明,该算法能够准确分割出目标,保留丰富的细节内容,对低对比度图像和噪声图像也有很好的分割效果,获取的阈值相比于传统算法更优。

Image Threshold Segmentation Algorithm Based on SUSAN Edge Information

Because of the good performance in maintaining the target profile and partitioning the object from low-contrast pictures,threshold segmentation method based on edge information is widely used and especially suitable for industrial production images.But traditional methods are sensitive to noise,and the threshold is hard to select.To solve these problems,in this paper,an adaptive image threshold segmentation algorithm based on edge information was pre-sented.The proposed algorithm uses characteristic response of SUSAN to describe the edge information of pixels to suppress the effect of noise and weak boundaries.Time and space complexity is greatly reduced when using the min-max cut threshold segmentation algorithm based on graph spectral theory rather than other segmentation algorithms,and the received threshold is global optimum.Experimental results show that the algorithm can segment the target accurately and retain rich details at the same time,for low-contrast and noise images the algorithm also has good performance,and threshold is better than traditional algorithm.