论文标题
使用深神经网络检测原子云
Atom Cloud Detection Using a Deep Neural Network
论文作者
论文摘要
我们使用深层神经网络来检测并将利益区域盒放在超低原子云周围的吸收和荧光图像中，并能够在单个图像中识别和绑定多个云。神经网络还输出分割掩码，以识别每个云的大小，形状和方向，从中我们提取云的高斯参数。这允许将2D高斯拟合可靠地播种，从而实现全自动图像处理。
We use a deep neural network to detect and place region-of-interest boxes around ultracold atom clouds in absorption and fluorescence images---with the ability to identify and bound multiple clouds within a single image. The neural network also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds' Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing.
