特征提取以解决不确定性的指导

Boris Kovalerchuk, Michael Kovalerchuk, S. Streltsov, M. Best
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引用次数: 2

摘要

自动特征提取在图像理解中起着至关重要的作用。通常,图像分析比AFE算法更好地提取特征,因为分析使用了额外的信息。这些信息的提取和处理可能比原来的AFE任务更复杂,这就导致了“复杂性陷阱”。当建筑物的阴影引导建筑物和道路的提取时,就会发生这种情况。本文提出了一种利用GMTI/GPS跟踪信息和旧的不准确的道路和小径地图作为AFE指南提取道路和小径的AFE算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guidance in feature extraction to resolve uncertainty
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.
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