Earticle

초록

영어

Image segmentation is a fundamental and challenging problem in image processing and often a vital step for high level analysis. Considering of the inefficient curve evolution against weak boundary and intensity heterogeneous images, an improved level set segmentation framework guided by the image gradient function is proposed. In this framework, the edges and regions of the image are roughly divided by using the image gradient sample function. Compare to the Local Binary Fitting (LBF) model, local and global intensity fitting (LGIF) model, and Edge-flow based active contour model, this algorithm may improve efficient of curve evolution in a large extent. After that, we compare this algorithm with the other active contour model to show that segmenting the noisy blurry boundary and intensity heterogeneous images can be achieved, and still go on an in-depth comparison of these models. Finally, we show the results on challenging images to illustrate the accurate segmentations.

목차

Abstract
 1. Introduction
 2. The Review and Discussion of the Related Works
 3. The Proposed Model
 4. Various Gradient Measures
 5. Experimental Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Bo Cai [ China Academy of Engineering Physics, Mianyang, Sichuan 621000, China, Southwest University of Science & Technology, Mianyang Sichuan 621010, China ]
• Zhigui Liu [ China Academy of Engineering Physics, Mianyang, Sichuan 621000, China, Southwest University of Science & Technology, Mianyang Sichuan 621010, China ]
• Junbo Wang [ China Academy of Engineering Physics, Mianyang, Sichuan 621000, China, Southwest University of Science & Technology, Mianyang Sichuan 621010, China ]
• Yuyu Zhu [ Southwest University of Science & Technology, Mianyang Sichuan 621010, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605