Abstract

Understanding the mechanisms underlying visual object recognition has been an important subject in both human and machine vision since the early days of cognitive science. Current state-of-the-art machine vision systems can perform only rudimen-tary tasks in highly constrained situations compared to the powerful and exible recognition abilities of the human visual system. In this work, we provide an algorithmic analysis of psychological and anatomical models of the ventral visual pathway, more speci cally the pathway that is responsible for expert object recognition, using the current state of machine vision technology. As a result, we propose a biologically plausible expert object recognition system composed of a set of distinct component subsystems performing feature extraction and pattern matching. The proposed system is evaluated on four di erent multi-class data sets, compar-ing the performance of the system as a whole to the performance of its component subsystems alone. The results show that the system matches the performance of state-of-the-art machine vision techniques on uncompressed data, and performs bet-

Citations