Real-Time Object Recognition Based on Cortical Multi-scale Keypoints

In recent years, a large number of impressive object categorisation algorithms have surfaced, both computational and biologically motivated. While results on standardised benchmarks are impressive, very few of the best-performing algorithms took run-time performance into account, rendering most of them useless for real-time active vision scenarios such as cognitive robots. In this paper, we combine cortical keypoints based on primate area V1 with a state-of-the-art nearest neighbour classifier, and show that such a system can approach state-of-the-art categorisation performance while meeting the real-time constraint.