Unsupervised Texture Segmentation via Applying Geodesic Active Regions to Gaborian Feature Space
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Summary
A novel variational method for unsupervised texture segmentation using a Gabor filter bank to extract texture features and a framework of geodesic active regions is applied based on them.
- Type
- article
- Published
- 2007-03-01
- Cited by
- 9
- References
- 20
- Access
- Open access
- OpenAlex
- https://openalex.org/W42557438
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:12266676
Keywords
Geodesic, Artificial intelligence, Segmentation, Pattern recognition (psychology), Texture (cosmology)
References
- Texture segmentation via a diffusion-segmentation scheme in the Gabor feature space
- Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
- Level set methods: an overview and some recent results
- Optimal Gabor filter design for texture segmentation using stochastic optimization
- Geodesic active regions for supervised texture segmentation
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Deformable boundary finding in medical images by integrating gradient and region information
- Active unsupervised texture segmentation on a diffusion based feature space
- Geodesic Active Contours
- The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
- PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 8
- Color Snakes
- Nonlinear Matrix Diffusion for Optic Flow Estimation
- Active Regions for Unsupervised Texture Segmentation Integrating Region and Boundary Information
- Geodesic Active Contours Applied to Texture Feature Space
- World Academy of Science, Engineering and Technology 38 2010 A Numerical Study on Thermal Dissociation of
- ROTATION AND SCALE INVARIANT TEXTURE CLASSIFICATION
Cited by
- Outils et méthodes d'analyse d'images 3D texturées : application à la segmentation des images échographiques. (Tools and methods of analysis for 3D textured images : application to ultrasound images segmentation)
- Textured Image Segmentation based on Local Spectral Histogram and Active Contour
- Level Set Based Segmentation Using Local Feature Distribution
- LBP-guided active contours
- Texture Segmentation of Natural Images Based on Active Contour Model
- Active Contours Based Battachryya Gradient Flow for Texture Segmentation
- Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow
- Textured Image Segmentation Using Active Contours
- Bimodal Texture Segmentation with the Lee-Seo Model
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