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Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information
- Source :
- Journal of Applied Mathematics, Vol 2014 (2014), J. Appl. Math.
- Publication Year :
- 2014
- Publisher :
- Hindawi Publishing Corporation, 2014.
-
Abstract
- Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
- Subjects :
- Active contour model
Article Subject
business.industry
Applied Mathematics
lcsh:Mathematics
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation
Pattern recognition
Image segmentation
Machine learning
computer.software_genre
lcsh:QA1-939
Texture (geology)
Image (mathematics)
Image texture
Computer Science::Computer Vision and Pattern Recognition
Piecewise
Segmentation
Artificial intelligence
business
computer
Mathematics
ComputingMethodologies_COMPUTERGRAPHICS
Subjects
Details
- Language :
- English
- ISSN :
- 1110757X
- Database :
- OpenAIRE
- Journal :
- Journal of Applied Mathematics
- Accession number :
- edsair.doi.dedup.....b7e29eedc73f17e00806c269f6947e5e
- Full Text :
- https://doi.org/10.1155/2014/614613