A neuromorphic system for visual object recognition

Autonomous object recognition in images and videos is a topic of emerging importance in commercial and defense applications. Recent advances in the field of visual neuroscience are increasingly being leveraged to develop biologically-plausible models and algorithms for visual cognition with the ultimate goal of improving object recognition accuracy, speed, and robustness. In this paper, we describe our feasibility attempt at building one such end-to-end neuromorphic visual object recognition architecture and system that is inspired by neuroscience theories of human visual attention and recognition. Our system, called the NVRS (Neuromorphic Visual Recognition System), processes images and videos to first detect and localize objects and then classify them into one of several pre-defined object classes. The NVRS uses modularized algorithms inspired by the Form-and-Color-and-Depth (FACADE) and Laminar-ART (LAMINART) models of human visual attention, search, and object recognition. The NVRS is primarily composed of two encapsulated modules: attention and object recognition. The system was evaluated on simulated scenes, Caltech-101 and COIL-100 datasets, and aerial imagery, with results that demonstrate the efficacy of the NVRS. Lessons learnt from the NVRS can be useful in building practical autonomous visual systems for real-world applications and towards addressing longer-term BICA challenges.