12 research outputs found
TernausNetV2: Fully Convolutional Network for Instance Segmentation
The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV
Feature Pyramid Network for Multi-Class Land Segmentation
Semantic segmentation is in-demand in satellite imagery processing. Because
of the complex environment, automatic categorization and segmentation of land
cover is a challenging problem. Solving it can help to overcome many obstacles
in urban planning, environmental engineering or natural landscape monitoring.
In this paper, we propose an approach for automatic multi-class land
segmentation based on a fully convolutional neural network of feature pyramid
network (FPN) family. This network is consisted of pre-trained on ImageNet
Resnet50 encoder and neatly developed decoder. Based on validation results,
leaderboard score and our own experience this network shows reliable results
for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge.
Moreover, this network moderately uses memory that allows using GTX 1080 or
1080 TI video cards to perform whole training and makes pretty fast
predictions
On scattering of solitons for the Klein-Gordon equation coupled to a particle
We establish the long time soliton asymptotics for the translation invariant
nonlinear system consisting of the Klein-Gordon equation coupled to a charged
relativistic particle. The coupled system has a six dimensional invariant
manifold of the soliton solutions. We show that in the large time approximation
any finite energy solution, with the initial state close to the solitary
manifold, is a sum of a soliton and a dispersive wave which is a solution of
the free Klein-Gordon equation. It is assumed that the charge density satisfies
the Wiener condition which is a version of the ``Fermi Golden Rule''. The proof
is based on an extension of the general strategy introduced by Soffer and
Weinstein, Buslaev and Perelman, and others: symplectic projection in Hilbert
space onto the solitary manifold, modulation equations for the parameters of
the projection, and decay of the transversal component.Comment: 47 pages, 2 figure