Knowledge-Based Interpretation of Utility Maps

Hartog, J.E. den; Kate, T.K. ten; Gerbrands, Jan J.

doi:10.1006/cviu.1996.0007

Cited by 30 publications

(14 citation statements)

References 28 publications

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“…The map interpretation system of den Hartog et al [15] also appears very similar to ANON. It employs an object-oriented, semantic net representation of drawing entities and their interrelationships and is based around a three-component, cyclic control structure.…”

Section: Comparing Structured Systems At the Knowledge Levelmentioning

confidence: 71%

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Interpreting Line Drawing Images: A Knowledge Level Perspective

Pridmore

Darwish

Elliman

2002

Lecture Notes in Computer Science

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Abstract. Image understanding systems rely heavily on a priori knowledge of their application domain, often exploiting techniques developed in the wider field of knowledge-based systems (KBSs). Despite attempts, typified by the KADS/CommonKADS projects, to develop structured knowledge engineering approaches to KBS development, those working in image understanding continue to employ unstructured 1 st generation KBS methods. We analyse some existing image understanding systems, concerned with the interpretation of images of line drawings, from a knowledge engineering perspective. Attention focuses on the relationship between the structure of the systems considered and the KADS/CommonKADS models of expertise, sometimes called generic task models. Mappings are identified between each system and an appropriate task model, identifying common inference structures and use of knowledge. This is the first step in the acquisition of models of the expertise underpinning drawing interpretation. Such models would bring significant benefits to the design, maintenance and understanding of line drawing interpretation systems.

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Section: Comparing Structured Systems At the Knowledge Levelmentioning

confidence: 71%

“…Two drawing interpretation systems which comprise similar components communicating in similar ways are the mechanical drawing interpretation system of Joseph and Pridmore [13] and the utility map interpretation system of den Hartog, ten Kate and Gerbrands [15].…”

Section: Comparing Structured Systems At the Knowledge Levelmentioning

confidence: 99%

Interpreting Line Drawing Images: A Knowledge Level Perspective

Pridmore

Darwish

Elliman

2002

Lecture Notes in Computer Science

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“…The original positional error of analogue geographic information is incremented during the digitalisation process that consists of a preliminary scanning of paper sheets, followed by a feature extraction step. Different errors are introduced when a manual (Bolstad et al 1990) or an automatic process is used (Den Hartog et al 1996). Subsequently, some editing tasks like line simplification (Douglas and Peucker 1973;Vereign 2000) and generalisation (Blakemore 1984;João 1995) might introduce errors that must be considered.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the uncertainty of the length of energy distribution networks

Scaioni

2010

Appl Geomat

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A statistical evaluation of the uncertainty of the length of a utility network is frequently omitted in current geographic information system (GIS) applications, although this information is important for management purposes. Nevertheless, the spatial database storing geographic information about the network might have been set up through a complex process (ground surveying, digitising of analogue maps, integration of different data sources), resulting in a dataset with unclassified uncertainty. This paper proposes a practical but 'rigorous' approach to evaluate the influence of the uncertainty in the determination of the length of a network. Starting from the definition of length uncertainty in terms of simple geometric elements (arc, pair of arcs, open and closed path), some rules for its evaluation in the case of complex networks have been developed. The statistical concept applied for the evaluation of uncertainty is quite standard because it is based on the covariance propagation theory. On the other hand, the influence of correlations between the lengths of adjacent elements has been considered as well. The application of this method only requires the topological structure of the network that can be built in a GIS environment. Furthermore, an alternative 'approximate' method is proposed to find an approximation of the uncertainty of the network length with a simple formula, which requires neither any particular processing step nor the network topology. Both methods have been tested and validated on simulated and real datasets.

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“…This is especially true for those approaches (e.g., Neural Network based approaches [1]) that require large sets of pattern samples for pre-training. Similarly, the syntaxes and structures of the patterns should also be pre-defined before recognition in syntactic and structural approaches (e.g., [2]) and the knowledge about the patterns should also be pre-acquired before recognition in knowledge-based approaches (e.g., [2] and [8]). For example, if the task is to recognize lines from images, the attributes or features of the line patterns should be analyzed such that appropriate representations and algorithm can be designed and implemented.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, the features, syntaxes, and other knowledge about the patterns, e.g., the graphic geometry, are hard-coded in the recognition algorithms. Hence, currently, each graphics recognition algorithm only deals with a limited set of specific, known graphic patterns, e.g., dimension-sets [2], shafts [8]. Once implemented and incorporated in a graphics recognition system, these features, syntaxes, and knowledge cannot be changed.…”

Section: Introductionmentioning

confidence: 99%

Example-Driven Graphics Recognition

Wenyin

2002

Lecture Notes in Computer Science

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Abstract. An example-driven graphics recognition scheme is presented, which is an extension of the generic graphics recognition algorithm we presented years ago. The key idea is that, interactively, the user can specify one or more examples of one type of graphic objects in an engineering drawing image, an the system then learn the constraint rules among the components in this type of graphic objects and recognize similar objects in the same drawing or similar drawings by matching the constraint rules. Preliminary experiments have shown that this is a promising way for interactive graphics recognition.

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Knowledge-Based Interpretation of Utility Maps

Cited by 30 publications

References 28 publications

Interpreting Line Drawing Images: A Knowledge Level Perspective

Interpreting Line Drawing Images: A Knowledge Level Perspective

Evaluation of the uncertainty of the length of energy distribution networks

Example-Driven Graphics Recognition

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