NLEBS: automatic target detection using a unique nonlinear-enhancement-based system in IR images

Shlomo Greenberg, Raanan Yehezkel, Yaniv Gurevich, Hugo Guterman

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A new automatic target detection method for IR images that only requires information about the size of the targets is described. The proposed nonlinear-enhancement-based system (NLEBS) algorithm is based on a nonlinear enhancement paradigm that increases the contrast of the targets with minimal change in the clutter's contrast. The NLEBS employs several stages of processing, each with a different operational purpose. First, the nonlinear enhancement operation is performed by using an iterative procedure. After binarization, segmentation merging causes each local image region to grow by filling in holes. Then segmentation pruning is applied to remove spurious segments. Finally, a heuristic-based metric is employed to validate the possible targets. The performance of the NLEBS was tested with a large set of IR images. The results of these experiments showed a probability of detection greater than 90% and a false-alarm rate of about 1 false alarm per image.

Original languageEnglish
Pages (from-to)1369-1376
Number of pages8
JournalOptical Engineering
Volume39
Issue number5
DOIs
StatePublished - 1 Jan 2000

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (all)

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