Automatic Verification of Roads in Digital Images Using Profiles

Wiedemann, Christian; Mayer, Helmut

doi:10.1007/978-3-642-80294-2_63

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…This is another important research topic to be resolved. Figure 9 98.5% 96.2% 94.7% Figure 10 (a) 98.8% 99.3% 98.1% Figure 10 ( Basing on the method developed by Wiedemann (1996) for evaluating automatic road extraction systems, we use three indexes to assess the quality of the generated road network. The completeness is defined as the percentage of the correctly extracted data over the reference data and the correctness represents the ratio of correctly extracted road data.…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

“…An automatic road verification approach based on digital aerial images as well as GIS data is developed in (Wiedemann & Mayer, 1996) as a part of the update procedure for GIS data. The candidates for roadsides, which are obtained by searching the surroundings of GIS road-axes in the image based on profiles, are tested, and a measure of confidence is also calculated.…”

Section: Rural Road Extraction Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Jin¹,

Miska²,

Chung³

et al. 2012

Remote Sensing - Advanced Techniques and Platforms

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

Section: Rural Road Extraction Techniquesmentioning

confidence: 99%

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Jin¹,

Miska²,

Chung³

et al. 2012

Remote Sensing - Advanced Techniques and Platforms

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“…To reduce this error, the roadsides were predicted by fitting a parabola to the most recent road points, as described in [13]. This error can also be avoided by detecting weak edges from gradient images using distance limits [14]. Another type of error comes from disturbances on the road that have not been removed.…”

Section: Comparing the Performance Of Human And Computer Vision-basedmentioning

confidence: 99%

Understanding Human-Computer Interactions in Map Revision

Zhou

Bischof

Caelli

2004

Lecture Notes in Computer Science

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Abstract. It is difficult to track, parse and model human-computer interactions during editing and revising of documents, but it is necessary if we are to develop automated technologies that will aid or replace humans. This paper introduces a system for accessing and recording a stream of events related to human actions in a real-time cartographic map revision system. The recorded events are parsed into a sequence of meaningful user actions and an action representation in XML format is generated. We also report results of experiments on predicting user actions such as view changes, edits, road tracking/production using hidden Markov models.

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“…Even though for GIS update, i.e., verification and acquisition, old GIS data can make object extraction easier, the proposed approach is restricted to object acquisition (here: roads) without using prior GIS information (for work in this area see, e.g., Refs. 1,2). This is done because automatic acquisition shows the potential and limits of an extraction scheme and can deepen the understanding of image interpretation.…”

Section: Introductionmentioning

confidence: 99%

<title>Semantic objects and context for finding roads</title>

Baumgärtner¹,

Steger²,

Mayer³

et al. 1997

SPIE Proceedings

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This paper presents a multi-resolution approach for automatic extraction of roads from digital aerial imagery. Roads are modeled as a network of intersections and links between the intersections. For different context regions, i.e., rural, forest, and urban areas, the model describes different relations between background objects, e.g., buildings or trees, and semantic road objects, e.g., road-parts, road-segments, road-links, and intersections. The classification of the image into context regions is done by texture analysis. The approach to detect roads is based on the extraction of edges in a high resolution image and the extraction of lines in an image of reduced resolution. Using both resolution levels and explicit knowledge about roads, hypotheses for roadsides are generated. The roadsides are used to construct quadrilaterals representing road-parts and polygons representing intersections. Neighboring road-parts are chained to road-segments. Road-links, i.e., the roads between two intersections, are built by grouping of road-segments and closing of gaps between road-segments. Road-links are constructed using knowledge about context.

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Automatic Verification of Roads in Digital Images Using Profiles

Cited by 5 publications

References 2 publications

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Understanding Human-Computer Interactions in Map Revision

<title>Semantic objects and context for finding roads</title>

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