Neural Networks for the Texture Classification of Segmented Regions of Forward Looking Infrared Images | Zendy

J.F. Haddon | Zendy; J.F. Boyce | Zendy; S.R. Protheroe | Zendy; Simon Hesketh | Zendy

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Neural Networks for the Texture Classification of Segmented Regions of Forward Looking Infrared Images

Author(s) -

J.F. Haddon,

J.F. Boyce,

S.R. Protheroe,

Simon Hesketh

Publication year - 1993

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.7.20

Subject(s) - artificial intelligence , artificial neural network , computer science , pattern recognition (psychology) , texture (cosmology) , computer vision , pixel , hermite polynomials , enhanced data rates for gsm evolution , feature (linguistics) , image texture , infrared , feature extraction , feature vector , matrix (chemical analysis) , image (mathematics) , image segmentation , mathematics , optics , physics , materials science , mathematical analysis , linguistics , philosophy , composite material

Texture can be interpreted as a measure of the 'edginess' about a pixel and can thus be described by edge co-occurrence matrices. The matrix can be decomposed using 2-dimensional orthogonal Hermite functions, the coefficients of which provide a low order feature vector which is characteristic of the texture. The Hermite coefficients for 240 hand-segmented regions of grass, trees, sky and river from 60 forward looking infrared (FLIR) images have been used to train and validate 2 neural networks, which have subsequently been used to label FLIR images segmented using co-occurrence techniques [1].

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