Applications of adaptive feature-specific imaging

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationVisual Informaion Processing XVI
StatePublished - Nov 19 2007
EventVisual Information Processing XVI - Orlando, FL, United States
Duration: Apr 10 2007Apr 10 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherVisual Information Processing XVI
Country/TerritoryUnited States
CityOrlando, FL


  • Bayes rule
  • Feature-specific imaging
  • Principal component analysis
  • Sequential hypotheses testing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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