Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Posteriori Error Analysis for the Mean Curvature Flow of Graphs
SIAM Journal on Numerical Analysis
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Dual Norms and Image Decomposition Models
International Journal of Computer Vision
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Identification of grain boundary contours at atomic scale
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Modeling interaction for segmentation of neighboring structures
IEEE Transactions on Information Technology in Biomedicine
Edge Detection by Adaptive Splitting
Journal of Scientific Computing
Robust edge detection using mumford-shah model and binary level set method
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Edge Detection by Adaptive Splitting II. The Three-Dimensional Case
Journal of Scientific Computing
Hi-index | 0.00 |
Nowadays image acquisition in materials science allows the resolution of grains at atomic scale. Grains are material regions with different lattice orientation which are frequently in addition elastically stressed. At the same time, new microscopic simulation tools allow to study the dynamics of such grain structures. Single atoms are resolved experimentally as well as in simulation results on the data microscale, whereas lattice orientation and elastic deformation describe corresponding physical structures mesoscopically. A qualitative study of experimental images and simulation results and the comparison of simulation and experiment requires the robust and reliable extraction of mesoscopic properties from the microscopic image data. Based on a Mumford---Shah type functional, grain boundaries are described as free discontinuity sets at which the orientation parameter for the lattice jumps. The lattice structure itself is encoded in a suitable integrand depending on a local lattice orientation and one global elastic displacement. For each grain a lattice orientation and an elastic displacement function are considered as unknowns implicitly described by the image microstructure. In addition the approach incorporates solid---liquid interfaces. The resulting Mumford---Shah functional is approximated with a level set active contour model following the approach by Chan and Vese. The implementation is based on a finite element discretization in space and a step size controlled, regularized gradient descent algorithm.