36,846 research outputs found
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2017
Visualizing 2D Flows with Animated Arrow Plots
Flow fields are often represented by a set of static arrows to illustrate
scientific vulgarization, documentary film, meteorology, etc. This simple
schematic representation lets an observer intuitively interpret the main
properties of a flow: its orientation and velocity magnitude. We propose to
generate dynamic versions of such representations for 2D unsteady flow fields.
Our algorithm smoothly animates arrows along the flow while controlling their
density in the domain over time. Several strategies have been combined to lower
the unavoidable popping artifacts arising when arrows appear and disappear and
to achieve visually pleasing animations. Disturbing arrow rotations in low
velocity regions are also handled by continuously morphing arrow glyphs to
semi-transparent discs. To substantiate our method, we provide results for
synthetic and real velocity field datasets
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