An Adaptive Thresholding Algorithm Based on Grayscale WaveTransformation for Industrial Inspection Images
工业检测图像灰度波动变换自适应阈值分割算法

The industrial inspection images are usually under non-uniform illumination, and local adaptive thresholding algorithms have better segmentation performance on them than the global ones. But the local algorithms based on image's sub-blocks are short of instructions for partitioning, and the local algorithms based on pixel's neighborhood will probably cause some misclassifications within the background or foreground. To resolve these problems, a novel adaptive thresholding algorithm based on multi-directional grayscale wave transformation is proposed in this paper. Firstly, it performs the transformation by grayscale waves in multi-directions to get a matrix of multi-dimensional vectors. Secondly, the vectors are compressed to one dimension using the principal component analysis (PCA) method, and then the Otsu global method is employed to find optimal wave threshold for segmentation on this matrix. This algorithm does not need partitioning the image any more and only takes the peak height threshold and the boolean background color as its two parameters. Experiments demonstrate that this method has a excellent capability of decreasing the influence of non-uniform illumination in industrial inspection images, and its segmentation performance is better than several other local thresholding algorithms, such as Niblack's method and Sauvola's method.