Computer scientists at the University of California, San Diego, have combined sophisticated computer vision algorithms and a brain-computer interface to find mines in sonar images of the ocean floor. The study shows that the new method speeds detection up considerably, when compared to existing methods--mainly visual inspection by a mine detection expert.
"Computer vision and human vision each have their specific strengths, which combine to work well together," said Ryan Kastner, a professor of computer science at the Jacobs School of Engineering at UC San Diego.
"For instance, computers are very good at finding subtle, but mathematically precise patterns while people have the ability to reason about things in a more holistic manner, to see the big picture. We show here that there is great potential to combine these approaches to improve performance."
Researchers worked with the U.S. Navy's Space and Naval Warfare Systems Center Pacific (SSC Pacific) in San Diego to collect a dataset of 450 sonar images containing 150 inert, bright-orange mines placed in test fields in San Diego Bay. An image dataset was collected with an underwater vehicle equipped with sonar. In addition, researchers trained their computer vision algorithms on a data set of 975 images of mine-like objects.
In the study, researchers first showed six subjects a complete dataset, before it had been screened by computer vision algorithms. Then they ran the image dataset through mine-detection computer vision algorithms they developed, which flagged images that most likely included mines.
They then showed the results to subjects outfitted with an electroencephalogram (EEG) system, programmed to detect brain activity that showed subjects reacted to an image because it contained a salient feature--likely a mine. Subjects detected mines much faster when the images had already been processed by the algorithms. Computer scientists published their results recently in the IEEE Journal of Oceanic Engineering.
The algorithms are what's known as a series of classifiers, working in succession to improve speed and accuracy. The classifiers are designed to capture changes in pixel intensity between neighboring regions of an image. The system's goal is to detect 99.5 percent of true positives and only generate 50 percent of false positives during each pass through a classifier. As a result, true positives remain high, while false positives decrease with each pass.
Researchers took several versions of the dataset generated by the classifier and ran it by six subjects outfitted with the EEG gear, which had been first calibrated for each subject. It turns out that subjects performed best on the data set containing the most conservative results generated by the computer vision algorithms. They sifted through a total of 3,400 image chips sized at 100 by 50 pixels.
Each chip was shown to the subject for only 1/5 of a second (0.2 seconds) --just enough for the EEG-related algorithms to determine whether subject's brain signals showed that they saw anything of interest.
All subjects performed better than when shown the full set of images without the benefit of prescreening by computer vision algorithms. Some subjects also performed better than the computer vision algorithms on their own.
"Human perception can do things that we can't come close to doing with computer vision," said Chris Barngrover, who earned a computer science Ph.D. in Kastner's research group and is currently working at SSC Pacific. "But computer vision doesn't get tired or stressed. So it seemed natural for us to combine the two."
In addition to Barngrover and Kastner, co-authors on the paper include Paul DeGuzman, a program manager at Neuromatters LLC, and Alric Althoff, a Ph.D. student in computer science at the Jacobs School of Engineering at UC San Diego. Neuromatters is a pioneer in brain-computer interface technologies with their C3Vision™ system, which was adapted for use in this project. The researchers also would like to thank Advanced Brain Monitoring, a medical devices company, for the use of the company's EEG headset.
Ioana Patringenaru | EurekAlert!
Researchers build transistor-like gate for quantum information processing -- with qudits
17.07.2019 | Purdue University
New DFG Research Group "Metrology for THz Communications"
17.07.2019 | Technische Universität Braunschweig
Adjusting the thermal conductivity of materials is one of the challenges nanoscience is currently facing. Together with colleagues from the Netherlands and Spain, researchers from the University of Basel have shown that the atomic vibrations that determine heat generation in nanowires can be controlled through the arrangement of atoms alone. The scientists will publish the results shortly in the journal Nano Letters.
In the electronics and computer industry, components are becoming ever smaller and more powerful. However, there are problems with the heat generation. It is...
Scientists have visualised the electronic structure in a microelectronic device for the first time, opening up opportunities for finely-tuned high performance electronic devices.
Physicists from the University of Warwick and the University of Washington have developed a technique to measure the energy and momentum of electrons in...
Scientists at the University Würzburg and University Hospital of Würzburg found that megakaryocytes act as “bouncers” and thus modulate bone marrow niche properties and cell migration dynamics. The study was published in July in the Journal “Haematologica”.
Hematopoiesis is the process of forming blood cells, which occurs predominantly in the bone marrow. The bone marrow produces all types of blood cells: red...
For some phenomena in quantum many-body physics several competing theories exist. But which of them describes a quantum phenomenon best? A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.
Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking...
An international research group led by scientists from the University of Bayreuth has produced a previously unknown material: Rhenium nitride pernitride. Thanks to combining properties that were previously considered incompatible, it looks set to become highly attractive for technological applications. Indeed, it is a super-hard metallic conductor that can withstand extremely high pressures like a diamond. A process now developed in Bayreuth opens up the possibility of producing rhenium nitride pernitride and other technologically interesting materials in sufficiently large quantity for their properties characterisation. The new findings are presented in "Nature Communications".
The possibility of finding a compound that was metallically conductive, super-hard, and ultra-incompressible was long considered unlikely in science. It was...
24.06.2019 | Event News
29.04.2019 | Event News
17.04.2019 | Event News
19.07.2019 | Physics and Astronomy
19.07.2019 | Physics and Astronomy
19.07.2019 | Earth Sciences