Titlebook: Analysis of Engineering Drawings and Raster Map Images; Thomas C. Henderson Book 2014 Springer Science+Business Media New York 2014 AMAM.

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,Kommunikationsplattformen – Foren,es good clusters and an average of 78% recall and 70% precision; GT clustering achieves good clusters and 83% recall with 74% precision. Finally, EM forms very good clusters and has an average 86% recall and 71% precision.

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Other Semantic Feature Segmentation,es good clusters and an average of 78% recall and 70% precision; GT clustering achieves good clusters and 83% recall with 74% precision. Finally, EM forms very good clusters and has an average 86% recall and 71% precision.

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tic engineering drawing and map analysis techniques.Covers d.This book covers up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps. The Non-Deterministic Agent System (NDAS) offers a parallel computational approach to such image analysis.

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Hannah Wirths,Karin Reber,Christian W. Glück drawings to be analyzed. We present our approach to these problems here, and this development has been done in the context of the semantic analysis of scanned images of mechanical CAD engineering drawings.

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Vanessa Kubek,Annette Blaudszun-Lahmspecific color encoding of features (e.g., USGS maps may use from 6 to 13 specific colors), geo-spatial information, and names of features may be known and sought in the image (e.g., rivers, lakes, roads, towns, etc.).

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