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Abstract-This paper systematically reviews 10 years of research that several Army Laboratories conducted in object recognition algorithms, processors, and evaluation techniques. In the military, object recognition is applied to the discrimination of military targets, ranging from human-aided to autonomous operations, and is called Automatic Target Recognition (ATR). The research described here has been concentrated in human-aided target recognition applications, but some attention has been paid to automatic processes. Definitions and performance metrics that have been developed are described along with performance data showing the present state-of-the-art. The effects of signal-to-noise and clutter parameters are indicated in the data. Multisensor fusion and model-based algorithms are discussed as the latest techniques under consideration by the military research community. The results demonstrate that useful performance can be achieved, and tools are evolving to understand and improve the performance under real-world conditions. The referenced research strongly indicates the need for the development of image science, as described in the paper, to support the theoretical underpinnings of ATR.

Abstract. Aided and automatic target recognition (Ai/ATR) capability is a critical technology needed by the military services for modern combat. However, the current level of performance that is available is largely deficient compared to the requirements. This is largely due to the difficulty of acquiring targets in realistic environments but has also been due to the difficulty in getting new concepts from, for example, the academic community, due to limitations for distribution of classified data. The difficulty of the performance required has limited the fulfillment of the promise that is so anticipated by the war fighter. We review the metrics, imagery data bases, and sensors associated with Ai/ATR performance and suggest possible technical approaches that could enable new advancements in military-relevant performance. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

This paper provides a 40-year review of the infrared imaging system modeling activities at the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD). The result of these modeling activities is a system model that describes the target acquisition performance of a human observer and an infrared imager. The model has been adopted by the military infrared imaging community as an assessment of how well an ensemble of observers perform the tasks of target detection, recognition, and identification. The model is used in infrared imager design and assessment, where military users understand how the metrics predicted by the model relates to system performance on the battlefield. This review begins with early work in the late 1950s and proceeds to present day modeling successes. Finally, the infrared imaging system modeling activities for the future are discussed.

This is a technical historical chronicle of the past and on-going development of performance models for electro-optical sensors carried out by the U.S. Army CECOM NVESD, the original Night Vision Laboratory. The emphasis has been on thermal imaging models and is also the focus ofthis paper. The origin ofthe Johnson criteria is shown and the resulting models that have evolved from the original concept proposed by John Johnson. The present formulations ofthe models are detailed and the newest developments are introduced. The force that drives the various improvements in the models is the development ofmore sophisticated thermal imagers whose performance must be described and predicted. Background supporting developments in laboratory measurements and field validation are indicated.