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Proceedings Paper

Texture classification of segmented regions of forward-looking infrared images
Author(s): John F. Haddon; James Frederick Boyce
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Paper Abstract

This paper presents new techniques for the texture classification of regions based on edge co- occurrence matrices and discrete Hermite functions which are used to describe them. The paper briefly defines co-occurrence matrices and how they can be used to describe the relationship of edges around a pixel. Texture is interpreted as a measure of the edginess about a pixel and is described by edge co-occurrence matrices. The texture of the region is characterized by an orthogonal decomposition of the co-occurrence matrix using 2-dimensional discrete Hermite functions. The coefficients of this decomposition provide a low order feature vector which can be used for texture classification. The coefficients of the Hermite functions used in the decomposition of the co-occurrence matrix are analyzed by two neural network classifiers: the multilayer perceptron and the cascade correlation. Experiments have been performed for the training and validation of the networks on two types of terrain (grass and trees) taken from FLIR images during a low level approach to a bridge.

Paper Details

Date Published: 20 October 1993
PDF: 12 pages
Proc. SPIE 1957, Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition, (20 October 1993); doi: 10.1117/12.161432
Show Author Affiliations
John F. Haddon, Defence Research Agency (United Kingdom)
James Frederick Boyce, King's College London (United Kingdom)


Published in SPIE Proceedings Vol. 1957:
Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition
Lynn E. Garn; Lynda Ledford Graceffo, Editor(s)

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