Adaptive feature-specific imaging for recognition of non-Gaussian classes

Pawan K. Baheti, Jun Ke, Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We present an adaptive feature-specific imaging (AFSI) system for application to an M-class recognition task. The proposed system uses nearest-neighbor-based density estimation to compute the (non-Gaussian) class-conditional densities. We refine the density estimates based on the training data and the knowledge from previous measurements at each step. The projection basis for the AFSI system is also adapted based on the previous measurements at each step. The decision-making process is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of error (Pe) and we compare the AFSI system with an adaptive-conventional (ACONV) system. The AFSI system exhibits significant improvement compared to the ACONV system at low signal-to-noise ratio (SNR), and it is shown that, for an M = 4 hypotheses, SNR = -10 dB, and a desired Pe = 10-2, the AFSI system requires 30 times fewer measurements than the ACONV system. Experimental results validating the AFSI system are presented.

Original languageEnglish (US)
Pages (from-to)5225-5239
Number of pages15
JournalApplied optics
Issue number28
StatePublished - Oct 1 2009

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


Dive into the research topics of 'Adaptive feature-specific imaging for recognition of non-Gaussian classes'. Together they form a unique fingerprint.

Cite this