Multistage foveal target detection system

Douglas C. McKee, Cesar Bandera

Research output: Contribution to journalConference articlepeer-review

Abstract

The premise of foveal vision is that surveying a large area with low resolution to detect regions of interest, followed by their verification with localized high resolution, is a more efficient use of computational and communications throughput than resolving the area uniformly at high resolution. This paper presents target/clutter discrimination techniques that support the foveal multistage detection and verification of infrared-sensed ground targets in cluttered environments. The first technique uses a back-propagation neural network to classify narrow field-of-view high acuity image chips using their projection onto a set of principal components as input features. The second technique applies linear discriminant analysis on the same input features. Both techniques include refinements that address generalization and detected region of interest position errors. Experimental results using second generation forward looking infrared imagery are presented.

Original languageEnglish (US)
Pages (from-to)194-203
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3374
DOIs
StatePublished - 1998
Externally publishedYes
EventSignal Processing, Sensor Fusion, and Target Recognition VII - Orlando, FL, United States
Duration: Apr 13 1998Apr 15 1998

All Science Journal Classification (ASJC) codes

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

Keywords

  • Classification
  • Discrimination
  • False alarm filtering
  • Foveal machine vision
  • Target detection

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