The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Riemannian Drums, Anisotropic Curve Evolution and Segmentation
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A general framework for low level vision
IEEE Transactions on Image Processing
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation
International Journal of Computer Vision
The Uncertainty Principle: Group Theoretic Approach, Possible Minimizers and Scale-Space Properties
Journal of Mathematical Imaging and Vision
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Gabor Analysis is frequently used for texture analysis and segmentation. Once the Gaborian feature space is generated it may be interpreted in various ways for image analysis and segmentation. Image segmentation can also be obtained via the application of "snakes" or active contour mechanism, which is usually used for gray-level images. In this study we apply the active contour method to the Gaborian feature space of images and obtain a method for texture segmentation. We calculate six localized features based on the Gabor transform of the image. These are the mean and variance of the localized frequency,orientation and intensity. This feature space is presented, via the Beltrami framework, as a Riemannian manifold. The stopping term, in the geodesic snakes mechanism, is derived from the metric of the features manifold. Experimental results obtained by application of the scheme to test images are presented.