Image thresholding method guided by maximizing similarity of multi-directional weighted intuitionistic fuzzy
To deal with the issues of poor segmentation accuracy and adaptability in existing thresholding segmentation methods,an image thresholding method guided by maximizing similarity of multi-directional weighted intuitionistic fuzzy is proposed.First,the proposed method utilizes convolution kernels based on first-order derivative of anisotropic Gaussian to perform multi-directional convolution operation and multi-scale product transformation on an input image,which will output four reference images with unimodal histogram in four directions.Then,it constructs the corresponding intuitionistic fuzzy sets by sampling four reference images with a binary contour image.Finally,it utilizes a multi-directional weighting strategy to fuse four intuitionistic fuzzy sets to construct a similarity objective function,and selects the gray level corresponding to the maximum value of this objective function as the segmentation threshold.The proposed method is comprehensively compared with 5 recent segmentation methods,and the experimental results on 8 synthetic images and 88 real-world images show that the proposed method has higher segmentation accuracy and more flexible adaptability,and the average Matthews correlation coefficients are 0.998 and 0.964 for the synthetic images and real-world ones,which outperform the second-best method by 39.90％and 26.22％,respectively.
image thresholdingfirst-order derivative of anisotropic gaussianmulti-scale product transformationintuitionistic fuzzy setssimilarity between images
万方数据
维普
