Analysis of infrared images based on grey system and neural network

Abstract

Purpose

The purpose of this paper is to develop a system to analyse the characteristics of infrared objects.

Design/methodology/approach

According to the gray scale of image pixel by the image entropy, gray scale estimating is carries on to construct the neural networks. And then the grey relational analysis and grey clustering methods are applied to filter the possible object. The target is predicted through image segmentation pretreatment based on the forecasting value by grey system and assigned corresponding mark. The forecasting precision is greatly elevated by GM (1, 1) model.

Findings

The paper illustrates that, based on the analysis and its experimental results, this system has a good recognition rate for infrared target.

Research limitations/implications

This paper provides a way to grasp the minutial feature of the image. The filtering operation based on pixel level provided auto‐adapted filtering with a new stage.

Practical implications

Applications of grey theory deepened the content of detecting infrared targets and enriched technology of image processing.

Originality/value

This system introduces an effective method for detecting infrared targets.

Citation

Heng, Z. (2010), "Analysis of infrared images based on grey system and neural network", Kybernetes, Vol. 39 No. 8, pp. 1366-1375. https://doi.org/10.1108/03684921011063673