Abstract
Feature-specific imaging (FSI) is a method by which non-traditional projections of object space may be computed directly in the optical domain. The resulting feature-specific measurements provide the advantages of reduced hardware complexity and improved measurement SNR. This SNR advantage translates into improved task (e.g., target recognition and/or tracking) performance. Adaptive FSI refers to any FSI system for which the results of previous measurements are used to determine future measurement basis vectors. This paper will describe an adaptive FSI system based on the sequential hypothesis testing approach. We will quantify the benefits of adaptation for a <i>M</i>-class recognition task, and present an extension of the AFSI system to incorporate null hypothesis.
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