基于证据的目标识别新技术:EB-ORS1

Q4 Computer Science

模式识别与人工智能 Pub Date : 1992-08-30 DOI:10.1109/ICPR.1992.201815

T. Caelli, Ashley Dreier

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引用次数: 2

摘要

作者扩展了Jain和Hoffman(1988)的基于证据的目标识别系统，包括一些新的与视图无关的特征，一个新的基于最小熵聚类的优化规则生成过程和一个估计最优证据权重并提供相关匹配过程的神经网络。这种方法为目标识别问题的难度提供了一个客观的定义。作者还用两套CAD(范围)模型对系统的程序和性能进行了评估。

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Some new techniques for evidence-based object recognition: EB-ORS1

The authors have extended the evidence-based object recognition system of Jain and Hoffman (1988) to include some new view-independent features, a new optimized rule generation procedure based upon minimum entropy clustering and a neural network which estimates optimal evidence weights and provides an associated matching procedure. This approach provides an objective definition of the difficulty of an object recognition problem. The authors also evaluate the procedures and performance of the system with two sets of CAD (range) models.<>

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来源期刊

模式识别与人工智能 Computer Science-Artificial Intelligence

CiteScore

1.60

自引率

0.00%

发文量

3316

期刊介绍：