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Curtis Becker

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Publications by Curtis Becker (bibliography)

1994

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MacMillan, Jean, Becker, Curtis, Kibbe, Marion and O'Kane, Barbara (1994): Integrating Human and Machine Vision: Lessons from Automated Target Recognition Systems. In: Proceedings of the Human Factors and Ergonomics Society 38th Annual Meeting 1994. pp. 1310-1311.

“The development of Automated Target Recognition (ATR) systems has been the focus of considerable interest and funding during the past decade. Such systems were originally envisioned as being almost completely autonomous and capable of detecting, locating, and classifying targets and of assigning weapons to targets with little or no human intervention. Such completely autonomous performance remains well beyond current ATR capabilities, however. Under the performance levels currently achievable for ATR systems, the human operator plays an essential role in screening ATR detections and rejecting false alarms. Operators must rapidly review the ATR's judgments, and their accuracy in confirming or rejecting those judgments is a critical determinant of the overall effectiveness of the human-machine system. The integration of human and machine visual capabilities is a key factor in effective system design. Research on human-ATR interaction has identified fundamental issues that must be considered in the design of any system in which human visual judgment is integrated with machine-based visual judgment. The objective of this panel is to identify and discuss critical issues for the design of an aided target recognition system, in which the human operator and the ATR work synergistically, and to assess the implications of human-ATR research results for the design of systems that integral human and machine vision. The panel will discuss the following issues: * Differences in human and ATR target-recognition accuracy. How accurate must an ATR be, relative to the human operator, in order to be of assistance? What are the implications of ATR false alarms versus missed detections? How do ATR performance levels relate to the confidence that human operators place in the ATR? * Differences in the process by which humans and ATR systems recognize targets, and the implications of those differences. Automated target recognition technologies need not replicate the process by which humans recognize targets, and human and machine vision may show different relative strengths and weaknesses, i.e., the ATR may be superior to the human in recognizing some types of targets in some circumstances but not in others. What are the implications of these differences for operator confidence in the ATR and for system design? * How important is the design of the visual interface in human-ATR interactions? What aspects of display design can affect human-ATR system performance? What are the implications of combining human image interpretation with ATR judgments that are displayed numerically, e.g., a numeric level of confidence associated with a target identification?”

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