Recognition using information-optimal adaptive feature-specific imaging

Pawan K. Baheti, Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


We present an information-theoretic adaptive feature-specific imaging (AFSI) system for a M-class recognition task. The proposed system utilizes the recently developed task-specific information (TSI) framework to incorporate he knowledge from previous measurements and adapt the projection matrix at each step. The decisionmaking ramework is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of misclassification (Pe), and we compare the performances of three approaches: the new TSI-based AFSI system, a previously reported statistical AFSI system, and static FSI (SFSI). The TSI-based AFSI system exhibits significant improvement compared with SFSI and statistical AFSI at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses, SNR=-20 dB and desired Pe=10-2, TSI-based AFSI requires 3 times fewer measurements than statistical AFSI, and 16 times fewer measurements than SFSI. We also describe an extension of the proposed method that is suitable for recognition in the presence of nuisance parameters such as illumination conditions and target orientations.

Original languageEnglish (US)
Pages (from-to)1055-1070
Number of pages16
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number4
StatePublished - Apr 1 2009

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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