Gray Level Image Threshold Using Neutrosophic Shannon Entropy
This addresses image segmentation for computer vision applications, but it appears incremental as it builds on existing neutrosophic and entropy-based approaches.
The authors tackled grayscale image segmentation by minimizing Shannon's neutrosophic entropy, resulting in a method that achieves good performance with optimal thresholds, as demonstrated on test images.
This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.