8,506 research outputs found
Neighbourhood-consensus message passing and its potentials in image processing applications
In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed
Complexity of Discrete Energy Minimization Problems
Discrete energy minimization is widely-used in computer vision and machine
learning for problems such as MAP inference in graphical models. The problem,
in general, is notoriously intractable, and finding the global optimal solution
is known to be NP-hard. However, is it possible to approximate this problem
with a reasonable ratio bound on the solution quality in polynomial time? We
show in this paper that the answer is no. Specifically, we show that general
energy minimization, even in the 2-label pairwise case, and planar energy
minimization with three or more labels are exp-APX-complete. This finding rules
out the existence of any approximation algorithm with a sub-exponential
approximation ratio in the input size for these two problems, including
constant factor approximations. Moreover, we collect and review the
computational complexity of several subclass problems and arrange them on a
complexity scale consisting of three major complexity classes -- PO, APX, and
exp-APX, corresponding to problems that are solvable, approximable, and
inapproximable in polynomial time. Problems in the first two complexity classes
can serve as alternative tractable formulations to the inapproximable ones.
This paper can help vision researchers to select an appropriate model for an
application or guide them in designing new algorithms.Comment: ECCV'16 accepte
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical
structures of the human brain in Magnetic Resonance (MR) image data. We draw
inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)
architecture for semantic segmentation of objects in natural images, and adapt
it to our task. Unlike previous CNN-based methods that operate on image
patches, our model is applied on a full blown 2D image, without any alignment
or registration steps at testing time. We further improve segmentation results
by interpreting the CNN output as potentials of a Markov Random Field (MRF),
whose topology corresponds to a volumetric grid. Alpha-expansion is used to
perform approximate inference imposing spatial volumetric homogeneity to the
CNN priors. We compare the performance of the proposed pipeline with a similar
system using Random Forest-based priors, as well as state-of-art segmentation
algorithms, and show promising results on two different brain MRI datasets.Comment: ISBI 2016: International Symposium on Biomedical Imaging, Apr 2016,
Prague, Czech Republi
- …