원문정보
초록
영어
This study presents a tri-level thresholding method for image segmentation with invasive weed optimization (IWO) algorithm. The objective of the proposed approach is to handle the nonextensivity and vagueness of image in segmentation, in the meanwhile to reduce the computation time. In this study, the histogram of image is converted to fuzzy domain by membership function firstly. Then the thresholding method is constructed through maximizing the sum of nonextensive entropy of subsets of the each part of fuzzy histogram. The IWO algorithm is used to search the optimal thresholds to reduce the computation time in the new method. Experiments on synthetic and real-world images are given to demonstrate the effectiveness of the proposed approach compared with the other methods.
목차
1. Introduction
2. Thresholding by Fuzzy Set and Nonextensive Entropy
2.1. Convert the Image Histogram to Fuzzy Domain
2.2. Thresholding through Nonextensive Fuzzy Entropy
3. Thresholds Selection using Invasive Weed Optimization
4. Experimental Results and Analysis
4.1. Performance Evaluation
4.2. Experiments on Real Images
5. Conclusion
Acknowledgements
References