Detecting Mines in Minefields With Linear Characteristics
Detecting Mines in Minefields With Linear Characteristics
复制标题
具有线性特征的雷场地雷探测
作者：
Daniel Walsh;A. Raftery
我们认为，使用航空图像探测雷区的问题。图像处理的第一阶段将图像简化为一组点，每个点代表一个可能的地雷。我们的任务是确定哪些是真正的地雷。我们假定雷场由近似平行的地雷排组成，地雷的分布是按照一种概率分布，这种概率分布鼓励均匀间隔的线性模式。噪声点被假定为泊松分布。我们构造了一个马尔可夫链蒙特卡罗算法来估计模型，并获得后验概率为每个点是地雷。该算法在几个真实的雷场数据集上取得了较好的效果。
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.