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Researchers train AI to attribute paintings based on detailed brushstroke analysis – Art Newspaper



A figure from the research paper "Discerning the painter’s hand: machine
learning on surface topography" showing four paintings analysed in row A, topographic data in row B and machine learning attributions of different areas of each canvas in row C Courtesy Case Western Reserve University
Art historians may have a new tool for settling the attribution of disputed paintings using artificial intelligence (AI) thanks to research by a cross-disciplinary team led by physicists at Case Western Reserve University in Cleveland, Ohio. The research, published in November in the journal Heritage Science, shows how machine learning analysis of small sections of topographical scans of paintings—some as tiny as half a millimeter—was able to attribute the works to the correct artist with up to 96% accuracy. The technology could eventually also help identify which artists were responsible for different areas of a painting made by multiple artists or produced by an artist’s workshop, and help tell authentic works from forgeries.
The project differs from others that have sought to harness AI to settle questions of attribution and authenticity in that most previous research in this area has been based on machine analysis of high-resolution images of paintings, not the painted surfaces of the canvases themselves.
“The idea was that analysing the brushstroke was going to create a fingerprint,” says Kenneth Singer, a physics professor at Case Western Reserve who led the research. “We found that even at the brush bristle level, there was a fair level of success in sorting the attribution. Frankly we don’t really understand that, it’s kind of mind boggling actually when you think about it, how the paint coming off a single bristle is indicative of what we’re calling the artist’s unintentional style.”
The project focused on analysis of sets of paintings made specially by students at the Cleveland Institute of Arts, who were tasked with painting copies of a photograph of a water lily. The research involved training convolutional neural networks (CNNs) with three-dimensional scans of the paintings’ surfaces made with a profilometer. By dividing the canvases into tiny square patches for analysis, the CNNs identified each artist’s “unintentional style” or “fingerprint”. The AI was then able to correctly attribute other paintings by matching the artists’ unintentional styles in the textures of brushstrokes.
The team behind the research is now looking for additional tests of its AI’s capabilities. It collaborated with conservation firm Factum Arte to analyze a topographic scan of El Greco’s Portrait of Juan Pardo de Tavera (1609), which was severely damaged during the Spanish Civil War and extensively restored.
“This is a painting we have an answer key to, because we have photos of the destroyed painting and the current painting, so we’re able to make a map of the areas that were conserved, and [the AI] was able to identify those areas,” Singers says. “But there was another section of the painting that it identified as conserved that wasn’t obvious, so we’re going to have a painting conservator in Spain look at the painting to see what’s going on.”
Now the research team is turning its attention to paintings produced by multiple artists trying to replicate the style of one painter in their studio or workshop. Discerning between the hand of a Renaissance master, that of his star pupil and those of his lesser-known assistants has long been a topic of heated debate among art historians and Old Masters specialists, often with large sums of money hanging in the balance when works go to auction. The researchers hope to develop “unbiased and quantitative methods to lend insight into disputed attributions of workshop paintings”, they write. To that end, they are working with artists from the Cleveland Institute of Art again to create brand new paintings in a workshop process, with multiple artists working on each canvas in a unified style.
In addition to the student painters and other members of Case Western Reserve’s physics department such as Michael Hinczewski, key collaborators on the research included the university’s chair of art history Elizabeth Bolman and Dean Yoder, the conservator of paintings at the Cleveland Museum of Art. The endeavour was a true marriage of art and science.
“The project came about from an idea of a student of mine, who at the time had just started dating an art history student,” Singer says. “They went to a conference on art and science and had the idea of using this profilometer we have in one of our labs for doing surface topography. I agreed to do it and then, after a while, all my students got involved and the collaboration grew. Those two students are married now, by the way.”
The next application for the AI could be to test it on media that have less surface texture than paintings, Singer says, like watercolours or drawings. “Those would seem to be more challenging,” he says, “but what I’ve learned in this project is that I shouldn’t be as skeptical as I usually am, because this artificial intelligence is surprisingly good.”


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What is an Ultrasound Tech Salary?



The job of an ultrasound technician can be a thrilling one. It is a promising career with high pay and advantages. A lot of technician schools offer training programs for the area. If you take the time to obtain a degree or certificate in the ultrasound field, you’ll be able to earn an income worth the effort and time.


Ultrasound salary rates are far more competitive than the majority of the positions in allied health. Some markets pay over $65,000 in this position. An ultrasound technician can expect to earn a median annual pay of $63,640 for diagnostic medical Sonographers as of May 2009, as per the Bureau of Labor Statistics.

The salary may vary based on the kind of job setting. For instance, the median annual income in 2009 for employees working in doctor’s offices was $63,820. For hospital workers, it was $63,770. The salaries can also differ according to state and clearly by country. Similar to other occupations, ultrasound wages are less for entry-level employees and higher for those in higher-paying posts. If you earn greater than $65,000 per year and 55 percent are over 50, as well as 22 percent are under 30, According to the Society of Diagnostic Medical Sonography.


Technicians can expect less money even if they’re not certified by the American Registry of Diagnostic Medical Sonographers (ARDMS). However, the bottom 10% of technicians earn the least money, equivalent to $ 43,990 a year, and the higher 10% earn $85,950 yearly. Pay is also contingent on the number of specificities the technician holds. The more specialties the technician has, the more lucrative the salary. Employer, certification/education, or background may also affect the ultrasound salary. Hospital employees make $63,770 a year.

Doctors employ the second highest number of ultrasound techs in their clinics, and 13,290 technicians earn $63,820 annually. Diagnostic and medical labs use the third highest number of technicians, i.e., approximately 4,680. They pay each one $61,820 of them each year. Schools that teach pay roughly $66,000.




Furthermore, geographic locations are also a significant factor in determining how much pay a technician can earn. For example, Massachusetts has the highest ultrasound technician’s salary, around $78,460 annually. Oregon is almost identical, with a salary of $78,320. Colorado is third with approximately $77,380 annually. The need for technicians is huge in not developed states, while jobs have attained a saturation point in more developed states.

If a technician wishes to make the most money, it is recommended that they join those firms that offer employment services. They could earn as much as $68,000 or more annually. Enterprises and management companies offer the second highest pay, $67,890 per annum. Outpatient centers earn $64,560 annually, while educational schools have a salary of approximately $66,000. The structure of wages is also influenced by the environment in which the technician works, the workload the technician is responsible for on their own, and the company’s size. Experience in the field is significant to the average salary.

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