3,478 research outputs found
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
Processing Color in Astronomical Imagery
Every year, hundreds of images from telescopes on the ground and in space are
released to the public, making their way into popular culture through
everything from computer screens to postage stamps. These images span the
entire electromagnetic spectrum from radio waves to infrared light to X-rays
and gamma rays, a majority of which is undetectable to the human eye without
technology. Once these data are collected, one or more specialists must process
the data to create an image. Therefore, the creation of astronomical imagery
involves a series of choices. How do these choices affect the comprehension of
the science behind the images? What is the best way to represent data to a
non-expert? Should these choices be based on aesthetics, scientific veracity,
or is it possible to satisfy both? This paper reviews just one choice out of
the many made by astronomical image processors: color. The choice of color is
one of the most fundamental when creating an image taken with modern
telescopes. We briefly explore the concept of the image as translation,
particularly in the case of astronomical images from invisible portions of the
electromagnetic spectrum. After placing modern astronomical imagery and
photography in general in the context of its historical beginnings, we review
the standards (or lack thereof) in making the basic choice of color. We discuss
the possible implications for selecting one color palette over another in the
context of the appropriateness of using these images as science communication
products with a specific focus on how the non-expert perceives these images and
how that affects their trust in science. Finally, we share new data sets that
begin to look at these issues in scholarly research and discuss the need for a
more robust examination of this and other related topics in the future to
better understand the implications for science communications.Comment: 10 pages, 6 figures, published in Studies in Media and Communicatio
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