计算机应用研究
計算機應用研究
계산궤응용연구
APPLICATION RESEARCH OF COMPUTERS
2013年
10期
3169-3171,3200
,共4页
图像处理%阈值分割%二维Otsu%类间方差%类内方差%边缘概率分布
圖像處理%閾值分割%二維Otsu%類間方差%類內方差%邊緣概率分佈
도상처리%역치분할%이유Otsu%류간방차%류내방차%변연개솔분포
image processing%threshold segmentation%2D Otsu%inter-class variance%intra-class variance%marginal probability distribution
为了提高二维阈值分割法处理速度, 提出了二维Otsu法的快速实现方法。基于二维随机变量的边缘概率分布, 将二维最佳阈值（s*, t*）的求解拆分成两个一维最佳阈值s*和t*的求解; 同时为了改善原算法的分割效果, 引入类内方差的定义, 提出了新的最佳阈值判别式。实验结果表明, 本方法不仅保留了原二维阈值法抗噪性强的特点, 其时间复杂度由O(L<sup>4</sup>)降为O(L), 空间复杂度由S(L<sup>2</sup>)降为S(L), 且分割错误率低于原二维Otsu法。该方法适合处理高斯噪声图像的快速阈值分割问题。
為瞭提高二維閾值分割法處理速度, 提齣瞭二維Otsu法的快速實現方法。基于二維隨機變量的邊緣概率分佈, 將二維最佳閾值（s*, t*）的求解拆分成兩箇一維最佳閾值s*和t*的求解; 同時為瞭改善原算法的分割效果, 引入類內方差的定義, 提齣瞭新的最佳閾值判彆式。實驗結果錶明, 本方法不僅保留瞭原二維閾值法抗譟性彊的特點, 其時間複雜度由O(L<sup>4</sup>)降為O(L), 空間複雜度由S(L<sup>2</sup>)降為S(L), 且分割錯誤率低于原二維Otsu法。該方法適閤處理高斯譟聲圖像的快速閾值分割問題。
위료제고이유역치분할법처리속도, 제출료이유Otsu법적쾌속실현방법。기우이유수궤변량적변연개솔분포, 장이유최가역치（s*, t*）적구해탁분성량개일유최가역치s*화t*적구해; 동시위료개선원산법적분할효과, 인입류내방차적정의, 제출료신적최가역치판별식。실험결과표명, 본방법불부보류료원이유역치법항조성강적특점, 기시간복잡도유O(L<sup>4</sup>)강위O(L), 공간복잡도유S(L<sup>2</sup>)강위S(L), 차분할착오솔저우원이유Otsu법。해방법괄합처리고사조성도상적쾌속역치분할문제。
In order to improve 2D thresholding algorithm's processing speed, this paper presented a fast implementation method of 2D Otsu. Based on marginal probability distribution of bivariate discrete random variable, it calculated two 1D optimal threshold, s*and t*, and then kept (s*, t*) as the optimal 2D Otsu threshold. Furthermore, in order to improve the segmentation of the original algorithm, this paper introduced the definition of intra-class variance and proposed a new discriminant. The experimental results show that the proposed algorithm outperforms original algorithm. Without losing the robustness to noise, its time complexity is reduced from O(L4) to O(L), space complexity is reduced from S(L<sup>2</sup>) to S(L), and the misclassification rate is lower. The method is suitable for handling the fast threshold segmentation of Gaussian noise images.
