AUTOREGRESSIVE SHAPE CLASSIFIERS IN TIME-VARYING NOISE

被引：2

作者：

GLENDINNING, RH

机构：

[1] Defence Research Agency (DRA), Malvern, Worcester

来源：

PATTERN RECOGNITION | 1993年 / 26卷 / 02期

关键词：

AUTOREGRESSIVE PROCESS; SHAPE; CLASSIFICATION; ROBUSTNESS;

D O I：

10.1016/0031-3203(93)90041-T

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Good results have been reported in a number of recent studies where auto-regressive shape classifiers have been used to allocate objects to a pre-determined number of classes. It is shown that this approach may give unsatisfactory results when applied to imaging systems whose noise characteristics vary substantially over time. Such variation may be due to changes in atmospheric conditions or the relative effect of discretization errors. Observations are based on the reconstruction of a number of noisy synthetic templates.

引用

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页码：325 / 331

页数：7

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