NASA's Artemis program aims to send astronauts to the south pole of the Moon, where water in the form of ice has been confirmed, rather than near the equator as with the Apollo mission. The dark areas are likely to contain more ice than the others but also to be dangerous for the astronauts venturing there. A team of researchers studied these areas using deep learning, the study entitled "Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone" was published in Geophysical Research Letters. For the first Artemis lunar missions, the selected astronauts (one man and one woman) will fly to the south pole of the moon. This region has a great potential, it is thought to have the greatest abundance of water ice because it has craters where the sun's rays never penetrate, their temperature is estimated at -170 .

But can some of the same tools--specifically those that power computer vision--be used to remove the drudgery and long hours VFX artists face when it comes to rotoscoping? Oscar Estrada, a recent graduate of the Rochester Institute of Technology's Motion Picture Science program, was determined to find out. Estrada presented his research Oct. 21, the opening day of the SMPTE 2019 Annual Technical Conference and Exhibition at the Westin Bonaventure Hotel in downtown Los Angeles. In his presentation, "Rotoscope Automation with Deep Learning," Estrada laid out his thesis research looking at whether the use of a convolutional neural network could be used to extract a person or persons from a video clip without human intervention. Convolutional neural networks are particularly well-suited to the task of rotoscoping as opposed to other neural network techniques, especially when large images like 4K are at play, he said.