Implicit Active Contours Driven by Local Binary Fitting Energy
Vanderbilt University · The Ohio State University
Abstract
Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major…
Citation impact
- FWCI
- 31.24
- Percentile
- 100%
- References
- 20
Authors
4Topics & keywords
- Active contour model
- Piecewise
- Artificial intelligence
- Computer science
- Image segmentation
- Kernel (algebra)
- Image (mathematics)
- Computer vision
- Affordable and clean energy