원문정보
초록
영어
Human object extraction from infrared image has broad applications, and has become an active research area in image processing community. Combined with chaos differential evolution (CDE) algorithm and morphological operators, a novel infrared human target extraction method is proposed based on nonextensive fuzzy entropy. Firstly, the image was transformed into a fuzzy domain by fuzzy membership function, and the image nonextensive fuzzy entropy was constructed. Then, the image was segmented by thresholding based on the maximum entropy principle and the pseudoadditivity rule of nonextensive entropy. In order to reduce the search time of optimal threshold selection, the CDE algorithm was presented. Finally, the object was extracted using morphological operators to denoise, fill cavity on the threshold segmented image. Experimental results show that the proposed method is efficient and requires less computation time.
목차
1. Introduction
2. The Principle of Object Extraction
2.1. Nonextensive Entropy
2.2. Image as a fuzzy set
2.3. Image nonextensive fuzzy entropy
2.4. Image segmentation
2.5. Object extraction
3. Chaos Differential Evolution Algorithm
4. Experimental Results and Analysis
5. Conclusions
Acknowledgements
References