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.
Hooded rats were raised in the dark and compared to light-reared controls. Twenty-one-day-old animals were not different from controls in the discrimination of 4-in. depths, but 30-day-old animals were deficient. The 60day-old animals were deficient in discriminating 4-, 6-, and 8-in. depths. The 30-day-old animals recovered the loss in depth discrimination after 48 hr. in the light. The 60-day-old animals had no noticeable recovery to the 4-in. depth discrimination, but they did recover discrimination of the 6-in. depth after 72 hr. in the light.
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