Computing persistent homology of large image data with Cubicle

Persistent homology is becoming increasingly used in practice. In particular, this is true in the context of (medical) image analysis and computer vision. However, specialized algorithms are necessary to efficiently handle large multidimensionalimage/voxel/cubical data. I will describe some mathematical and algorithmic techniques that I found useful in my 15-year-old journey towards making such computations efficient. I will also overview the state of the art, and mention a bit of the (messy) history. Additionally, I would like to ask the participants what they consider a ‘large’ point cloud and a ‘large’ 3D image. So please make up your mind – but don’t search for an answer if you’re not sure.