Name
Abstract | ||
|---|---|---|
This paper presents a new level-set method based on global and local regions for image segmentation. First, the image fitting term of Chan and Vese (CV) model is adapted to detect the image's local information by convolving a Gaussian kernel function. Then, a global term is proposed to detect large gradient amplitude at the outer region. The new energy function consists of both local and global terms, and is minimized by the gradient descent method. Experimental results on both synthetic and real images show that the proposed method can detect objects in inhomogeneous, low-contrast, and noisy images more accurately than the CV model, the local binary fitting model, and the Lankton and Tannenbaum model. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S021800141255004X | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Image segmentation, active contour model, Chan and Vese model, local binary fitting model | Local binary fitting,Image segmentation,Artificial intelligence,Gaussian function,Amplitude,Active contour model,Computer vision,Gradient descent,Pattern recognition,Level set method,Real image,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 1 | 0218-0014 |
Citations | PageRank | References |
4 | 0.43 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Qian Zhao | 1 | 92 | 9.98 |
| Xiao-Fang Wang | 2 | 52 | 1.62 |
| Frank Y. Shih | 3 | 1103 | 89.56 |
| Gang Yu | 4 | 4 | 0.43 |