A new recognition model for electronic architectural drawings

Lü, Tong; Tai, Chiew-Lan; Su, Feng; Cai, Shaobin

doi:10.1016/j.cad.2004.11.004

Cited by 58 publications

(34 citation statements)

References 16 publications

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“…Lewis [22] presented a system to build 3D polygonal models from floor plans. Lu [23] designed a system to construct detailed interior structure from computer-generated construction drawings for buildings. But they all failed to handle raster images of drawings.…”

Section: Related Workmentioning

confidence: 99%

Drawing-Based Procedural Modeling of Chinese Architectures

Hou

Qin

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper presents a novel modeling framework to build 3D models of Chinese architectures from elevation drawing. Our algorithm integrates the capability of automatic drawing recognition with powerful procedural modeling to extract production rules from elevation drawing. First, different from the previous symbol-based floor plan recognition, based on the repetitive pattern trees, small horizontal repetitive regions of the elevation drawing are clustered in a bottom-up manner to form architectural components with maximum repetition, which collectively serve as building blocks for 3D model generation. Second, to discover the global architectural structure and its components' interdependencies, the components are structured into a shape tree in a top-down subdivision manner and recognized hierarchically at each level of the shape tree based on Markov Random Fields (MRF). Third, shape grammar rules can be derived to construct the 3D semantic model and its possible variations with the help of a 3D component repository. The salient contribution lies in the novel integration of procedural modeling with elevation drawing, with a unique application to Chinese architectures.

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Section: Related Workmentioning

confidence: 99%

Drawing-Based Procedural Modeling of Chinese Architectures

Hou

Qin

2012

IEEE Trans. Visual. Comput. Graphics

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“…As previously mentioned, how to represent the contextual knowledge that describes drawing conventions is admittedly a hard problem [3], [4], [38]. For high-level interpretation of complex engineering drawings, the challenge of designing an efficient and robust knowledge representation lies with how to organize well a large number of potential engineering entities and their relations, especially their unpredictable nonlinear cross referencing.…”

Section: Hierarchical Knowledge Representation Methodsmentioning

confidence: 99%

“…Earlier, our research group designed a series of low-level algorithms for engineering drawing analysis [18], [22], [23], [24], [25]. We later continued our research to high-level recognition and 3D reconstruction [26], [38], [39], [47], [48]. This paper presents our new knowledge-based system for high-level interpretation of complex real-life engineering drawings.…”

Section: Related Workmentioning

confidence: 99%

“…Existing engineering interpretation methods may be roughly classified into five categories, depending on the basic technique they rely on: pixel-level knowledge-independent [6], [7], [9], [10], [11], [22], [23], pixel-level knowledge-dependent [13], [16], [17], [18], [20], [24], [25], [26], [27], [28], [29], vectorial-level knowledge-independent [30], [31], [32], [33], vectorial-level knowledge-dependent [34], [35], [36], [37], [38], [39], and hybrid systems [8], [40], [41].…”

Section: Related Workmentioning

confidence: 99%

“…Comparing with previous interpretation systems for engineering drawings [20], [30], [35], [36], [38], [48], our system offers the following novelties: First, to the best of our knowledge, our system represents the first effort in high-level interpretation real-life complex engineering drawings. Second, rather than using "rules + inference" representations, we devise a descriptor-based knowledge representation method through analyzing the human's interpretation processes of complex high-level engineering drawings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

Lü

Tai

Yang

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

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A Case‐based Decision Support System for Flooding Crises Onboard Ships

Ölçer

Majumder

2006

Quality & Reliability Eng

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This paper presents the development of an integrated system, namely the COMAND-DSS (Decision Support System created under project COMAND), for crisis management and real-time decision support in shipboard flooding emergencies. It employs the case-based reasoning approach to provide swift and effective decisionmaking support for shipboard personnel during an emergency on ship. The developed system, which focuses on decision support for flooding control, is also thought to be useful as a crew training system. In order to deal with a flooding damage in the COMAND-DSS, a multiple attribute decision-making methodology-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-is used to be able to choose the set of counter-flooding tanks that should be filled in order to achieve an optimal response to a flooding accident. The COMAND-DSS is composed of a knowledge base, a reasoning process and a graphical user interface for the shipboard personnel to interact with the system. The friendly windows-based user interface of the COMAND-DSS enables the user to take full advantage of the capabilities of the system in order to make quick decisions. Using this system, when a crisis occurs the optimal decisions and responses can be made in a short time, thereby reducing the risk to life, property and environment.

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A new recognition model for electronic architectural drawings

Cited by 58 publications

References 16 publications

Drawing-Based Procedural Modeling of Chinese Architectures

Drawing-Based Procedural Modeling of Chinese Architectures

A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation

A Case‐based Decision Support System for Flooding Crises Onboard Ships

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