Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our ultra-fast GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial.

本文提出了一个统一的框架到（i）找到球，（ii）预测姿势，（iii）在团队体育场景中分段播放器的实例掩码。这些问题对自动体育分析，生产和广播有高兴趣。常见做法是通过利用通用最先进的模型，例如Panoptic-Deeblab来单独解决每个问题，用于玩家分割。除了从单任务模型的乘法乘以增加的复杂性之外，由于团队体育场景的复杂性和特异性，使用现成的架子模型也会阻碍性能，如强大的遮挡和运动模糊。为了规避这些限制，我们的论文提出培训一种单一的模型，它通过组合零件强度场和空间嵌入原理来预测球和玩家掩模和姿势。部件强度场提供球和播放器位置，以及播放器接头位置。然后利用空间嵌入来将播放器实例像素联系到其各自的播放器中心，而且还将播放器接头分组成骷髅。我们展示了拟议模型在DeepSport篮球数据集上的有效性，为单独解决每个单独任务的SOA模型实现了可比性的性能。