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 M-class recognition task, and present an extension of the AFSI system to incorporate null
hypothesis.
KEYWORDS: Reconstruction algorithms, Target detection, Detection and tracking algorithms, Imaging systems, Image restoration, Sensors, Signal to noise ratio, Compressive imaging, Principal component analysis, Time metrology
Feature-specific imaging (FSI) refers to any imaging system that directly measures linear projections of an object irradiance distribution. Numerous reports of FSI (also called compressive imaging) using static projections can be found in the literature. In this paper we will present adaptive methods of FSI suitable for the applications of (a) image reconstruction and (b) target detection. Adaptive FSI for image reconstruction is based on Principal Component and Hadamard features. The adaptive algorithm employs an updated training set in order to determine the optimal projection vector after each measurement. Adaptive FSI for detection is based on a sequential hypothesis testing framework. The probability of each hypothesis is updated after each measurement and in turn defines a new optimal projection vector. Both of these new adaptive methods will be compared with static FSI. Adaptive FSI for detection will also be compared with conventional imaging.
We present a novel method for computing the information content of an image. We introduce the notion
of task-specific information (TSI) in order to quantify imaging system performance for a given task. This
new approach employs a recently-discovered relationship between the Shannon mutual-information and
minimum estimation error. We demonstrate the utility of the TSI formulation by applying it to several
familiar imaging systems including (a) geometric imagers, (b) diffraction-limiter imagers, and (c) projective/
compressive imagers. Imaging system TSI performance is analyzed for two tasks: (a) detection, and
(b) classification.
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