1 research outputs found
Variational Multi-Phase Segmentation using High-Dimensional Local Features
We propose a novel method for multi-phase segmentation of images based on
high-dimensional local feature vectors. While the method was developed for the
segmentation of extremely noisy crystal images based on localized Fourier
transforms, the resulting framework is not tied to specific feature
descriptors. For instance, using local spectral histograms as features, it
allows for robust texture segmentation. The segmentation itself is based on the
multi-phase Mumford-Shah model. Initializing the high-dimensional mean features
directly is computationally too demanding and ill-posed in practice. This is
resolved by projecting the features onto a low-dimensional space using
principle component analysis. The resulting objective functional is minimized
using a convexification and the Chambolle-Pock algorithm. Numerical results are
presented, illustrating that the algorithm is very competitive in texture
segmentation with state-of-the-art performance on the Prague benchmark and
provides new possibilities in crystal segmentation, being robust to extreme
noise and requiring no prior knowledge of the crystal structure