Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's
International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Automatic morphological detection of otolith nucleus
Pattern Recognition Letters
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Interpolating orientation fields: an axiomatic approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An axiomatic approach to image interpolation
IEEE Transactions on Image Processing
Fast computation of a contrast-invariant image representation
IEEE Transactions on Image Processing
Markov random field models for directional field and singularity extraction in fingerprint images
IEEE Transactions on Image Processing
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This paper copes with the reconstruction of accretionary growth sequence from images of biological structures depicting concentric ring patterns. Accretionary growth shapes are modeled as the level-sets of a potential function. Given an image of a biological structure, the reconstruction of the sequence of growth shapes is stated as a variational issue derived from geometric criteria. This variational setting exploits image-based information, in terms of the orientation field of relevant image structures, which leads to an original advection term. The resolution of this variational issue is discussed. Experiments on synthetic and real data are reported to validate the proposed approach.