论文标题
patchperpix例如分段
PatchPerPix for Instance Segmentation
论文作者
论文摘要
我们提出了一种新颖的方法，用于提议免费实例分割，该方法可以处理跨越图像的大部分的复杂对象形状，并形成带有交叉的密集对象簇。我们的方法基于预测密集的局部形状描述符，我们将其组装成形成实例。所有实例都一口气同时组装。据我们所知，我们的方法是第一个产生由学习形状斑块组成的实例的非著作方法。我们在各种数据域上评估了我们的方法，它在四个基准上定义了新的最新技术，即ISBI 2012 EM分割基准，BBBC010 C. Elegans Dataset和2D以及2D以及3D Cell核的荧光显微镜数据。我们还表明，我们的方法还适用于果蝇神经元的3D光显微镜数据，该数据表现出极端的复杂形状簇的情况
We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, namely the ISBI 2012 EM segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d fluorescence microscopy data of cell nuclei. We show furthermore that our method also applies to 3d light microscopy data of Drosophila neurons, which exhibit extreme cases of complex shape clusters
