Speeding up scene recognition by using an associative noise-like coding memory

The authors describe the use of an associative image classification architecture to estimate the camera-driven position of a land vehicle. Outdoor pictorial scenes are used within a vision system for the positioning of an autonomous land vehicle. Images supplied by sensorial input sources are matched with a set of simplified descriptions of possible scenes (prototypes). The associative classification module must supply a rough suggestion about the observed scene to a top-down expectation-driven recognition system. Easy training, low computation times, and ordering alternatives according to their evaluated reliabilities are some of the most important advantages of the approach described. Experimental results show that the classification mechanism also operates correctly in the presence of very similar training patterns. The system´s structural flexibility allows efficient application-oriented implementations on low-cost parallel machinery