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
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Gabor-Space Geodesic Active Contours
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A general framework for low level vision
IEEE Transactions on Image Processing
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
IEEE Transactions on Image Processing
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Anisotropic $\alpha$-Kernels and Associated Flows
SIAM Journal on Imaging Sciences
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Gabor feature space is elaborated for representation, processing and segmentation of textured images. As a first step of preprocessing of images represented in this space, we introduce an algorithm for Gabor feature space denoising. It is a geometric-based algorithm that applies diffusion-like equation derived from a minimal weighted area functional, introduced previously and applied in the context of stereo reconstruction models [6, 12]. In a previous publication we have already demonstrated how to generalize the intensity-based geodesic active contours model to the Gabor spatial-feature space. This space is represented, via the Beltrami framework, as a 2D Riemannian manifold embedded in a 6D space. In this study we apply the minimal weighted area method to smooth the Gabor space features prior to the application of the geodesic active contour mechanism. We show that this "Weighted Beltrami" approach preserves edges better than the original Beltrami diffusion. Experimental results of this feature space denoising process and of the geodesic active contour mechanism applied to the denoised feature space are presented.