论文标题
实例分割密集和重叠对象通过分层
Instance Segmentation of Dense and Overlapping Objects via Layering
论文作者
论文摘要
实例细分旨在描述图像中感兴趣的每个对象。最先进的方法通过分区语义分割或完善检测到的对象的粗略表示来实现此目标。在这项工作中，我们提出了一种新颖的方法来通过对象分层解决问题，即通过将拥挤的，甚至重叠的对象分配到不同的层中。通过将空间分离的对象分组在同一层中，可以通过在每个层中提取连接的组件来毫不费力地隔离实例。与以前的方法相比，我们的方法不受复杂对象形状或对象重叠的影响。随着后处理的最低，我们的方法在多种数据集上产生非常有竞争力的结果：秀丽隐杆线虫（BBBC），重叠的宫颈细胞（OCC）和培养的神经母细胞瘤细胞（CCDB）。源代码可公开可用。
Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not affected by complex object shapes or object overlaps. With minimal post-processing, our method yields very competitive results on a diverse line of datasets: C. elegans (BBBC), Overlapping Cervical Cells (OCC) and cultured neuroblastoma cells (CCDB). The source code is publicly available.
