Information measures for object recognition accommodating signaturevariability
Cooper, H.L.; Miller, M.I.
Information Theory, IEEE Transactions on
Volume 46, Issue 5, Aug 2000 Page(s):1896 - 1907
Digital Object Identifier 10.1109/18.857799
Summary:This paper presents measures characterizing the information
content of remote observations of ground scenes imaged via optical and
infrared sensors. Object recognition is posed in the context of
deformable templates; the special Euclidean group is used to accommodate
geometric variation of object pose. Principal component analysis of
object signatures is used to represent and efficiently accommodate
variation in object signature due to changes in the thermal state of the
object surface. Mutual information measures, which are independent of
the recognition system, are calculated quantifying both the information
gain due to remote observations of the scene and the information loss
due to signature variability. Signature model mismatch is quantitatively
examined using the Kullback-Leibler divergence. Expressions are derived
quadratically approximating the posterior conditional entropy on the
orthogonal group for high signal-to-noise ratio. It is demonstrated that
quadratic modules accurately characterize the posterior entropy for
broad ranges of signal-to-mise ratio. Information gain in
multiple-sensor scenarios is quantified, and it is demonstrated that the
cost of signature uncertainty for the Comanche series of FLIR images
collected by the US Army Night Vision Electronic Sensors Directorate is
approximately 0.8 bits with an associated near doubling of mean-squared
error uncertainty in pose
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