An enhanced image binarization method incorporating with Monte-Carlo simulation

We proposed an enhanced image binarization method. The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background, spatially-changed illumination, and uncertainties of block size in traditional method. The proposed method first partitions the image into square blocks that reflect local characteristics of the image. After image partitioning, each block is binarized using Otsu's thresholding method. To minimize the influence of the block size and the boundary effect, we incorporate Monte-Carlo simulation into the binarization algorithm. Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map, which illustrates the probability of each pixel classified as foreground. By setting a probability threshold, and separating foreground and background of the source image, the final binary image can be obtained. The described method has been tested by benchmark tests. Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.摘要本文基于蒙特卡洛模拟与局部阈值思想,提出了一种能够适应图像复杂背景、亮度不均条件的 灰阶图像二值化分割方法。该方法将灰阶图像划分为多个正方形子图像,每个子图像均反映了灰阶图 像的局部信息。先利用大津法对每个子图像进行二值化分割,再将所有二值化后的子图像重新合并后 形成灰阶图像的二值化结果。针对局部阈值分割过程中子图像的尺寸选取问题及二值化后子图像合并 时的边界效应问题,本文结合蒙特卡洛模拟思想提出了改进算法。将子图像尺寸作为蒙特卡洛计算步 中的随机变量,通过大量迭代计算获取灰阶图像中每个像素被分割为目标和背景的概率,并按照指定 概率阈值对其进行划分。为验证所述方法的可行性与准确性,本文依托多个灰阶图像案例对方法的二 值化结果进行了测试,结果表明,本文提出的方法能够较好地处理复杂背景及亮度不均条件下的灰阶 图像。本方法可为区域性遥感影像的解译和地物识别提供支撑。