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- W2069844218 abstract "We consider the problem of detecting minefields using aerial images. A first stage of image processing has reduced the image to a set of points, each one representing a possible mine. Our task is to decide which ones are actual mines. We assume that the minefield consists of approximately parallel rows of mines laid out according to a probability distribution that encourages evenly spaced, linear patterns. The noise points are assumed to be distributed as a Poisson process. We construct a Markov chain Monte Carlo algorithm to estimate the model and obtain posterior probabilities for each point being a mine. The algorithm performs well on several real minefield datasets." @default.
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- W2069844218 date "2002-02-01" @default.
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- W2069844218 title "Detecting Mines in Minefields With Linear Characteristics" @default.
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- W2069844218 doi "https://doi.org/10.1198/004017002753398308" @default.
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