A concurrent real-time biologically-inspired visual object recognition system

In this paper, we present an biologically-motivated object recognition system for robots and vision tasks in general. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization. We modify this static model to be usable in time-crucial real-world scenarios by applying methods for optimization from signal detection theory, information theory, signal processing and linear algebra. Our system is more robust to clutter and supports object localization by approaching the binding problem in contrast to previous models. We show that our model outperforms the preceding model and that by our modifications we created a robust and fast system which integrates the capabilities of biological-inspired object recognition in a technical application.