How to make a new algorithm for illuminating henry County, Maryland
The American Conservatives published a new paper on the topic of how to make an algorithm that can illuminate henrico county, Maryland.
The paper, “Illuminator for Henry County: The First Implementation,” was written by Edward H. Johnson, an assistant professor of engineering and a co-author on the paper.
Johnson’s paper is based on work he did with an MIT student, John K. Leong, who used machine learning to build an algorithm for generating high-quality images of Henry county from a digital camera image.
The goal of the algorithm is to highlight the local, historical, and cultural significance of a property or area, and to show that the images are accurate, according to the paper, which was published by the American Institute of Architects.
In the paper’s first iteration, Johnson’s algorithm was able to highlight a specific historic building in Henry, Maryland, based on the building’s shape, size, and its presence in the area.
The algorithm then created an image of the building using a dataset that showed the shape and size of the structure, including the facade, the location of windows, the width and height of the facade and the area it was in.
The resulting image of Henrys buildings was then projected onto a digital screen, and the algorithm was then able to generate an image that displayed all the buildings in the county.
The next iteration used the data from the first iteration and then ran another iteration on Henrys building to highlight it more accurately.
Johnson found that the algorithm generated an image which displayed the entire building, but the more buildings in Henrys, the less accurate it was.
In a follow-up paper, Johnson also used the same data to generate a map of Henries neighborhoods, and was able, for the first time, to create a map that accurately represented the neighborhood structure, Johnson said.
The map created by the algorithm can then be used to generate additional images of neighborhoods and landmarks.
“It’s a very interesting project,” Johnson said, “because you have to think in terms of history and geography, and it’s really a combination of machine learning and computer vision.”
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