Name
Abstract | ||
|---|---|---|
This paper puts forward a new method of level set image segmentation based on prior shape, which aims to provide a better solution to the challenging segmentation problems that typically occur in images with complex background, intensity inhomogeneity and partially blocked targets. First, we introduced glial cells into deep Boltzmann machine (DBM) to solve that units in the DBM layer are not connected to each other, and then the novel DBM is employed to learn prior shape. Next, we used the variational level set and the local Gaussian distribution to fit the image energy term with local mean and local variance of image. Then, the prior shape energy is integrated into the image energy term to construct the final energy segmentation model. The experimental results show that the new model has stronger robustness and higher efficiency for face images segmentation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s13748-018-00169-5 | Progress in Artificial Intelligence |
Keywords | Field | DocType |
Face segmentation,Improved DBM,Level set,Prior shape | Data mining,Boltzmann machine,Pattern recognition,Computer science,Segmentation,Local variance,Level set,Image segmentation,Robustness (computer science),Gaussian,Artificial intelligence,dBm | Journal |
Volume | Issue | ISSN |
8.0 | 2 | 2192-6360 |
Citations | PageRank | References |
0 | 0.34 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoling Wu | 1 | 54 | 10.26 |
| J. Zhao | 2 | 0 | 0.68 |
| Huibin Wang | 3 | 29 | 10.99 |