A novel fuzzy entropy approach to image enhancement and thresholding
Signal Processing
ICM Method for Multi-Level Thresholding Using Maximum Entropy Criterion
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
A technique of three-level thresholding based on probability partition and fuzzy 3-partition
IEEE Transactions on Fuzzy Systems
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
Hi-index | 0.10 |
In this paper, a novel fuzzy classification entropy approach to generic image thresholding is proposed. Under the assumption that the grayscale histogram of an image follows multimodal distribution, the fuzzy membership function is modified, and the fuzzy entropy is redefined, named fuzzy classification entropy (FCE), to indicate the fitness of the membership function to the actual histogram. The novel membership function and FCE consider not only inter-class distinctness, but also intra-class variety, which provides more accurate description of the histogram. We present bi-level and multi-level thresholding using ECE and conduct experiments on many grayscale images. The results show that the novel method can get moderate thresholds for most images, with better visual quality and less complexity than other fuzzy entropy based methods.