Multiresolution segmentation of forward looking IR and SAR imagery using neural networks

Hal Beck, Dan Bergondy, Joe Brown, Hamed Sari-Sarraf

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

A neural network approach to segmentation of forward looking infrared and synthetic aperture radar imagery is presented. This approach integrates three stages of processing. First, a wavelet transform of the image is performed by projection of the image onto a set of 2-D Gabor functions. This results in a multiple-resolution decomposition of the image into oriented, spatial frequency channels. Second, a neural network optimization procedure is used to estimate the wavelet transform coefficients. The third stage involves a segmentation technique that has been shown to work well on textures that human subjects readily segment into regions. Although the approach is still under development, preliminary results are promising. The direction of further research efforts are discussed.

Original languageEnglish
Pages (from-to)600-609
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1381
StatePublished - Jan 1 1991
EventIntelligent Robots and Computer Vision IX: Algorithms and Techniques - Boston, MA, USA
Duration: Nov 5 1990Nov 7 1990

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