Computer vision, the field of building computer algorithms to automatically understand the contents of images, grew out of AI and cognitive neuroscience around the 1960s. “Solving” vision was famously set as a summer project at MIT in 1966, but it quickly became apparent that it might take a little longer! The general image understanding task remains elusive 50 years later, but the field is thriving. Dramatic progress has been made, and vision algorithms have started to reach a broad audience, with particular commercial successes including interactive segmentation available as the “Remove Background” feature in Microsoft Office, image search, face detection and alignment, and human motion capture for Kinect. Almost certainly the main reason for this recent surge of progress has been the rapid uptake of machine learning ML over the last 15 or 20 years.
This first post in a two-part series will explore some of the challenges of computer vision and touch on the powerful ML technique of decision forests for pixel-wise classification.