Name
Title | ||
|---|---|---|
A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity |
Abstract | ||
|---|---|---|
Level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects/regions in a given image, provided that the underlying intensity has homogeneity. The case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well-known work of Chan–Vese (2001) and its many variants. However for images with intensity inhomogeneity or multiphases within the foreground of objects, such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful. To deal with such problems, in this paper, we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images. Tests and comparisons will show that our new model outperforms two previous models. Both synthetic and real life images are used to illustrate the reliability and advantages of our new model. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2015.08.022 | Pattern Recognition |
Keywords | Field | DocType |
Image segmentation,Calculus of variations,Level set method,Partial differential equations,Edges,Objects | Active contour model,Homogeneity (statistics),Curve fitting,Pattern recognition,Level set method,Segmentation,Level set,Image segmentation,Artificial intelligence,Mathematics,Piecewise | Journal |
Volume | Issue | ISSN |
51 | C | 0031-3203 |
Citations | PageRank | References |
18 | 0.59 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haider Ali | 1 | 84 | 15.04 |
| Noor Badshah | 2 | 37 | 3.98 |
| Ke Chen | 3 | 268 | 27.05 |
| Gulzar Ali Khan | 4 | 27 | 1.43 |