Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.662044
Title: Adaptive, linear, subspatial projections for invariant recognition of objects in real infrared images
Author: Smart, Michael Howard William
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1998
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Abstract:
In recent years computer technology has advanced to a state whereby large quantities of data can be processed. This advancement has fuelled a dramatic increased in research into areas of image processing which were previously impractical, such as automated vision systems for, both military, and domestic purposes. Automatic Target Recognition (ATR) systems are one such example of these automated processes. ATR is the automatic detection, isolation and identification of objects, often derived from raw video, in a real-world, potentially hostile environment. The ability to rapidly, and accurately, process each frame of the incoming video stream is paramount to the success of the system, in order to output suitable actions against constantly changing situations. One of the main functions of an ATR system is to identify correctly all the objects detected in each frame of data. The standard approach to implementing this component is to divide the identification process into two separate modules; feature extraction and classification. However, it is often difficult to optimise such a dual system with respect to reducing the probability of mis-identification. This can lead to reduced performance. One potential solution is a neural network that accepts image data at the input, and outputs estimated classification. Unfortunately, neural network models of this type are prone to misuse due to their apparent black box solutions. In this thesis a new technique, based on existing adaptive wavelet algorithms, is implemented that offers ease-of-use, adaptability to new environments, and good generalisation in a single image-in-classification-out model that avoids many of the problems of the neural network approach. This new model is compared with the standard two stage approach using real-world, infrared, ATR data. Various extensions to the model are proposed to incorporate invariance to particular object deformations, such as size and rotation, which are necessary for reliable ATR performance. Further work increases the flexibility of the model to further improve generalisation. Other aspects, such as data analysis and object generation accuracy, which are often neglected, are also considered.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.662044  DOI: Not available
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