Recovering data: Neural network model finds small objects in dense
images

Date:
August 4, 2020
Source:
National Institute of Standards and Technology (NIST)
Summary:
In efforts to automatically capture important data from scientific
papers, computer scientists have developed a method that can
accurately detect small, geometric objects such as triangles within
dense, low- quality plots contained in image data. Employing a
neural network approach designed to detect patterns, the model
has many possible applications in modern life.



FULL STORY ==========================================================================
In efforts to automatically capture important data from scientific papers, computer scientists at the National Institute of Standards and Technology (NIST) have developed a method that can accurately detect small, geometric objects such as triangles within dense, low-quality plots contained
in image data. Employing a neural network approach designed to detect
patterns, the NIST model has many possible applications in modern life.


========================================================================== NIST's neural network model captured 97% of objects in a defined set
of test images, locating the objects' centers to within a few pixels of manually selected locations.

"The purpose of the project was to recover the lost data in journal
articles," NIST computer scientist Adele Peskin explained. "But the study
of small, dense object detection has a lot of other applications. Object detection is used in a wide range of image analyses, self-driving
cars, machine inspections, and so on, for which small, dense objects
are particularly hard to locate and separate." The researchers took
the data from journal articles dating as far back as the early 1900s
in a database of metallic properties at NIST's Thermodynamics Research
Center (TRC). Often the results were presented only in graphical format, sometimes drawn by hand and degraded by scanning or photocopying. The researchers wanted to extract the locations of data points to recover
the original, raw data for additional analysis. Until now such data have
been extracted manually.

The images present data points with a variety of different markers,
mainly circles, triangles, and squares, both filled and open, of
varying size and clarity. Such geometrical markers are often used to
label data in a scientific graph. Text, numbers and other symbols,
which can falsely appear to be data points, were manually removed from
a subset of the figures with graphics editing software before training
the neural networks.

Accurately detecting and localizing the data markers was a challenge for several reasons. The markers are inconsistent in clarity and exact shape;
they may be open or filled and are sometimes fuzzy or distorted. Some
circles appear extremely circular, for example, whereas others do not
have enough pixels to fully define their shape. In addition, many images contain very dense patches of overlapping circles, squares, and triangles.

The researchers sought to create a network model that identified plot
points at least as accurately as manual detection -- within 5 pixels of
the actual location on a plot size of several thousand pixels per side.

As described in a new journal paper, NIST researchers adopted a network architecture originally developed by German researchers for analyzing biomedical images, called U-Net. First the image dimensions are contracted
to reduce spatial information, and then layers of feature and context information are added to build up precise, high-resolution results.

To help train the network to classify marker shapes and locate their
centers, the researchers experimented with four ways of marking the
training data with masks, using different-sized center markings and
outlines for each geometric object.

The researchers found that adding more information to the masks, such
as thicker outlines, increased the accuracy of classifying object shapes
but reduced the accuracy of pinpointing their locations on the plots. In
the end, the researchers combined the best aspects of several models
to get the best classification and smallest location errors. Altering
the masks turned out to be the best way to improve network performance,
more effective than other approaches such as small changes at the end
of the network.

The network's best performance -- an accuracy of 97% in locating object
centers -- was possible only for a subset of images in which plot
points were originally represented by very clear circles, triangles,
and squares. The performance is good enough for the TRC to use the neural network to recover data from plots in newer journal papers.

Although NIST researchers currently have no plans for follow-up studies,
the neural network model "absolutely" could be applied to other image
analysis problems, Peskin said.


========================================================================== Story Source: Materials provided by National_Institute_of_Standards_and_Technology_(NIST).

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. A. Peskin, B. Wilthan, and M. Majurski. Detection of Dense,
Overlapping,
Geometric Objects. International Journal of Artificial Intelligence
and Applications, 2020 DOI: 10.5121/ijaia.2020.11403 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200804122221.htm

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