11,881 research outputs found
Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function
Many nonlinear filters used in practise are stack filters. An algorithm is
presented which calculates the output distribution of an arbitrary stack filter
S from the disjunctive normal form (DNF) of its underlying positive Boolean
function. The so called selection probabilities can be computed along the way.Comment: This is the version published in Journal of Mathematical Imaging and
Vision, online first, 1 august 201
The output distribution of important LULU-operators
Two procedures to compute the output distribution phi_S of certain stack
filters S (so called erosion-dilation cascades) are given. One rests on the
disjunctive normal form of S and also yields the rank selection probabilities.
The other is based on inclusion-exclusion and e.g. yields phi_S for some
important LULU-operators S. Properties of phi_S can be used to characterize
smoothing properties of S. One of the methods discussed also allows for the
calculation of the reliability polynomial of any positive Boolean function
(e.g. one derived from a connected graph).Comment: 20 pages, up to trivial differences this is the final version to be
published in Quaestiones Mathematicae 201
Calculating the output distribution of stack filters that are erosion-dilation cascades, in particular LULU-filters
Original article available at http://arxiv.org/ENGLISH ABSTRACT: Two procedures to compute the output distribution 0S of certain stack filters S (so
called erosion-dilation cascades) are given. One rests on the disjunctive normal form of S
and also yields the rank selection probabilities. The other is based on inclusion-exclusion
and e.g. yields 0S for some important LULU-operators S. Properties of 0S can be used to
characterize smoothing properties.Preprin
Deep Learning as a Parton Shower
We make the connection between certain deep learning architectures and the
renormalisation group explicit in the context of QCD by using a deep learning
network to construct a toy parton shower model. The model aims to describe
proton-proton collisions at the Large Hadron Collider. A convolutional
autoencoder learns a set of kernels that efficiently encode the behaviour of
fully showered QCD collision events. The network is structured recursively so
as to ensure self-similarity, and the number of trained network parameters is
low. Randomness is introduced via a novel custom masking layer, which also
preserves existing parton splittings by using layer-skipping connections. By
applying a shower merging procedure, the network can be evaluated on unshowered
events produced by a matrix element calculation. The trained network behaves as
a parton shower that qualitatively reproduces jet-based observables.Comment: 26 pages, 13 figure
Introducing Geometry in Active Learning for Image Segmentation
We propose an Active Learning approach to training a segmentation classifier
that exploits geometric priors to streamline the annotation process in 3D image
volumes. To this end, we use these priors not only to select voxels most in
need of annotation but to guarantee that they lie on 2D planar patch, which
makes it much easier to annotate than if they were randomly distributed in the
volume. A simplified version of this approach is effective in natural 2D
images. We evaluated our approach on Electron Microscopy and Magnetic Resonance
image volumes, as well as on natural images. Comparing our approach against
several accepted baselines demonstrates a marked performance increase
Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face
images. Existing methods using a single exemplar lead to inaccurate results
when the exemplar does not contain sufficient stylized facial components for a
given photo. In this work, we propose an algorithm to stylize face images using
multiple exemplars containing different subjects in the same style. Patch
correspondences between an input photo and multiple exemplars are established
using a Markov Random Field (MRF), which enables accurate local energy transfer
via Laplacian stacks. As image patches from multiple exemplars are used, the
boundaries of facial components on the target image are inevitably
inconsistent. The artifacts are removed by a post-processing step using an
edge-preserving filter. Experimental results show that the proposed algorithm
consistently produces visually pleasing results.Comment: In CVIU 2017. Project Page:
http://www.cs.cityu.edu.hk/~yibisong/cviu17/index.htm
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