Integrated Vision and Sensing for Human Sensory Augmentation

Abstract' The Carnegie Mellon University MURI project sponsored by ONR performs multi-disciplinary research in integrating vision algorithms with sens- ing technology for low-power, low-latency, com- pact adaptive vision systems. These are crucial features necessary for augmenting the human sen- sory system and enabling sensory driven informa- tion delivery. The project spans four subareas ranging from low to high level of vision: (1) smart filters, based on the Acousto-Optic Tunable Filter (AOTF) technology; (2) computational sensor methodology, which integrates raw sensing and computation by means of VLSI technology; (3) neural-network based saliency identification tech- niques for identifying the most useful information for extraction and display; and (4) visual learning methods for automatic signal-to-symbol mapping. 1. Introduction Automated vision and sensing research has made great strides in the last 30 years. Yet vision systems still lack attributes shared by most successful mass- market technologies