Where computer vision, deep neural networks and combinatorial optimization meet
Understanding the brain is an old and yet-unsolved problem. To understand the workings of a neural circuit, it is possibly required to know its structure, and almost surely necessary to know its connectivity. After great progress in electron microscopy, several labs worldwide are milling away at animal brains and generating what will amount to petabytes of high-quality data. The resulting images are good enough for human tracers to consistently follow at least the majority of neural processes; unfortunately, humans would take thousands of years to complete the task for even the smallest mammalian brain. So the quest is on for computer vision algorithms to do the same automatically and reliably. The current state of the art pipelines recur to deep neural networks and combinatorial graph partitioning problems. The former are notoriously ill understood, the latter still expensive to solve at scale. In this talk, I will sketch the problem, a state of the art approach (which does not quite achieve human accuracy yet), and I will lay out some of the open problems in the field.
Donnerstag, den 20. Juli 2017 um 12:00 Uhr, in KIP INF 227, HS 2 Donnerstag, den 20. Juli 2017 at 12:00, in KIP INF 227, HS 2
Der Vortrag folgt der Einladung von The lecture takes place at invitation by Anna Marciniak-Czochra, Andreas Ott, Anna Wienhard