College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China.
PLoS One. 2021 Apr 30;16(4):e0251014. doi: 10.1371/journal.pone.0251014. eCollection 2021.
Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distinct foreground of the original image in which the interference of uneven illumination is effectively eliminated. Finally, the image can be effortlessly binarized by an existing global thresholding algorithm such as the Otsu method. In order to qualitatively and quantitatively verify the segmentation performance of the presented scheme, experiments were performed on a dataset collected from a nonuniform illumination environment. Compared with classical binarization methods, in some metrics, the experimental results demonstrate the effectiveness of the introduced algorithm in providing promising binarization outcomes and low computational costs.
虽然大多数工业应用中的图像目标较少且图像背景简单，但二值化仍然是一项具有挑战性的任务，由于光照不均匀的干扰，相应的结果通常不尽如人意。为了有效地对具有非均匀光照的图像进行阈值处理，本文提出了一种有效的全局二值化算法，该算法从高斯尺度空间（GSS）中的前 k 个主要成分构建原始图像的不均匀背景表面进行估计。然后，我们使用差分算子提取原始图像中明显的前景，有效消除了光照不均匀的干扰。最后，可以使用现有的全局阈值化算法（如 Otsu 方法）轻松地对图像进行二值化。为了定性和定量地验证所提出方案的分割性能，在从非均匀光照环境中收集的数据集上进行了实验。与经典的二值化方法相比，在某些指标上，实验结果表明，所提出的算法在提供有前景的二值化结果和低计算成本方面是有效的。
