원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.6
2014.12
pp.65-74
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The traditional Snake model and GVF-Snake model set high requirements on noise and initial contour in wood cell contour extraction. To solve this problem, on the premise of considering the image texture and gray-scale information, the area information is directly introduced into the active contour extraction model through force equilibrium equation. Experiments show that the contour extracted with this method is not only more close to real cell contour, but also improved in anti-noise property. In particular, in the convergence of high noise and deep sunken areas, it has some advantages not found in other traditional methods.
목차
Abstract
1. Introduction
2. Snake Contour Model
3. The Contour Extraction Model based on Image Texture and Gray-scale Information
3.1. Image Brightness and Texture Features
3.2. Texture Features
3.3. The Area Energy based on the Area Gray-scale of Images
3.4. The Area Force and Algorithm Principle based on the Green's Formula
4. Analysis of the Experimental Results
4.1. The Extraction of the Image Contour with Sunken Features
4.2. The Extraction of Image Contours Affected by Different Noises
4.3. The Extraction of Wood Slice Microscopic Cell Image
4.4. The Analysis of the Effect of the Algorithm
5. Conclusion
References
1. Introduction
2. Snake Contour Model
3. The Contour Extraction Model based on Image Texture and Gray-scale Information
3.1. Image Brightness and Texture Features
3.2. Texture Features
3.3. The Area Energy based on the Area Gray-scale of Images
3.4. The Area Force and Algorithm Principle based on the Green's Formula
4. Analysis of the Experimental Results
4.1. The Extraction of the Image Contour with Sunken Features
4.2. The Extraction of Image Contours Affected by Different Noises
4.3. The Extraction of Wood Slice Microscopic Cell Image
4.4. The Analysis of the Effect of the Algorithm
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보