Computer Vision News - November 2016

ECCV Daily: What is the Focal Flow work you are presenting, Emma? Emma: It is a new depth queue that can be measured very efficiently. Our model is just a thin lense camera that either is on a moving platform or observes a moving scene. We show that under Gaussian Blur, and Gaussian Blur only, there’s a very simple linear constraint that relates image derivatives to a four-vector which is a similar computation to optical flow with just a 4x4 linear system over each patch. ECCV Daily: Why is this work needed? Emma: We were inspired by this new generation of micro-scale platforms that’s being developed. Computer vision has done a great job at scaling up. We’re great with big data. We’re great with big systems with a lot of power and computation. We’re starting to find out how to sacrifice computation to work on smaller systems with smaller power budgets. There are robots being built now that work with fractions of milliwatts, microwatts. Computer vision doesn’t really have anything to offer them. We found a computational shortcut for depth measurement that will allow platforms in this class. We hope to be able to understand their scenes. ECCV Daily: What is the novelty in this work? Emma: I would say the novelty is the extreme efficiency of the computation. There are a lot of ways to measure depth. We’ve got stereo, Depth from Defocus, and there are a lot of ways to handle motion. “Computer vision doesn’t have anything to offer these robots” Among the many projects presented at ECCV2016 , RSIP Vision’s engineers picked one: 2 days later, it was awarded the Best Student Paper prize. Emma Alexander , a PhD student at Harvard University , gave us this interview the day before her exceptional Oral Session on Focal Flow: Measuring depth and velocity from defocus and differential motion . Our cue is monocular. It’s passive so we don’t change the camera. We don’t shine light onto the scene. Then our measurement algorithm is just a small linear system on image derivatives. We have very few adds and multiplies. We don’t have to expend electrical power on any other part of the sensor. Focal Flow - Emma Alexander, Harvard University 12 Our Pick at ECCV BEST OF ECCV