Earticle

초록

영어

GGAC is an improvement based on geodesic active contour model (GAC). GGAC model is a widely used method for image segmentation. But, it will be difficult to achieve satisfactory segmentation results to the texture, uneven structure, edge particles, weak edge and other features of the wood surface image. Therefore, the author proposes a segmentation method that integrates the improved Canny edge detection result integrated into the improved GGAC model redrawing boundary stop function, and uses the improved variational level set method to achieve the numerical solution. The algorithm has reduced the choice sensitivity to the initial contour and enhanced the scalability, which can make the profile curve converge to defect edges more rapidly, avoid the local optimum, and improve segmentation effects of weak edges and uneven image. The results are clearer, more consistent and real-time. It has provided a more effective way to segment the wood surface defects, and broadened the application scope of Canny operator and an improved geodesic active contour model.

목차

Abstract
 1. Introduction
 2. Canny Operator and GGAC Model Improvement
  2.1. Canny Operator Improvement
  2.2 Improvement of GGAC Model
 3. Combination Mode of Canny Operators and GGAC Model
  3.1. Reconstruction of Boundary Stopping Function
  3.2. Numerical Implementation by Using the Improved Variational Level Set Method
 4. Experimental Results and Discussion
 5. Conclusions
 References

키워드

저자

• Zhang Xianhong [ Electrical Engineering Department, Northeast Forestry University, China ]
• Zhang Chunrui [ Department of mathematical, Northeast Forestry University, China ] Corresponding Author

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