The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes.
This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given.
We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth.
Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals.
Experiments demonstrate a beneficial effect in both directions: adaptivity of labels improves image labeling, and steering label evolution by spatially regularized assignments leads to proper labels, because the assignment flow for supervised labeling is exactly used without any approximation for label learning.
We introduce a novel approach to Maximum A Posteriori inference based on discrete graphical models.
SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence.
We consider clustering problems where the goal is to determine an optimal partition of a given point set in Euclidean space in terms of a collection of affine subspaces.
Our energy is a non-convex, non-smooth higher-order vectorial total variation approach and promotes color consistent image filtering via a coupling term.
Sum-Product Networks with complex probability distribution at the leaves have been shown to be powerful tractable-inference probabilistic models.
We present a probabilistic graphical model formulation for the graph clustering problem.
We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution.
While the overall functional is non-convex, non-convexity is confined to a low-dimensional variable.
In the present paper we prove uniqueness for a larger class of problems and in particular independent of the image size.
no code implementations • 2 Apr 2014 • Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother
However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.
With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important.
We consider energy minimization for undirected graphical models, also known as MAP-inference problem for Markov random fields.
Shape from texture refers to the extraction of 3D information from 2D images with irregular texture.
Describing shapes by suitable measures in object segmentation, as proposed in , allows to combine the advantages of the representations as parametrized contours and indicator functions.
We present a novel variational approach to image restoration (e. g., denoising, inpainting, labeling) that enables to complement established variational approaches with a histogram-based prior enforcing closeness of the solution to some given empirical measure.