<title>Target cuing: a heterogeneous neural network approach</title>

Abstract

Autonomous analysis of complex image data is a critical technology in today's world of expanding automation. The growth of this critical field is slowed by problems in traditional image analysis methods. Traditional methods lack the speed, generality, and robustness that many modern image analysis problems require. While neural networks promise to improve traditional techniques, homogeneous neural network systems have difficulty performing all the diverse analysis required of an autonomous system. This paper proposes a dual-staged, heterogeneous neural network approach to image analysis; specifically, a way to solve the target cuing problem.