A new recognition model for electronic architectural drawings

Current methods for recognition and interpretation of architectural drawings are limited to either low-level analysis of paper drawings or interpretation of electronic drawings that depicts only high-level design entities. In this paper, we propose a Self-Incremental Axis-Net-based Hierarchical Recognition (SINEHIR) model for automatic recognition and interpretation of real-life complex electronic construction structural drawings. We design and implement a series of integrated algorithms for recognizing dimensions, coordinate systems and structural components. We tested our approach on more than 200 real-life drawings. The results show that the average recognition rate of structural components is about 90%, and the computation time is significantly shorter than manual estimation time.     

A complete system for the intelligent interpretation of engineering drawings

Converting paper-based engineering drawings into CAD model files is a tedious process. Therefore, automating the conversion of such drawings represents tremendous time and labor savings. We present a complete system which interprets such 2D paper-based engineering drawings, and outputs 3D models that can be displayed as wireframes. The system performs the detection of dimension sets, the extraction of object lines, and the assembly of 3D objects from the extracted object lines. A knowledge-based method is used to remove dimension sets and text from ANSI engineering drawings, a graphics recognition procedure is used to extract complete object lines, and an evidential rule-based method is utilized to identify view relationships. While these methods are the subject of several of our previous papers, this paper focuses on the 3D interpretation of the object. This is accomplished using a technique based on evidential reasoning and a wide range of rules and heuristics. The system is limited to the interpretation of objects composed of planar, spherical, and cylindrical surfaces. Experimental results are presented. Received December 2, 1998 / Revised June 18, 1999     

拓扑关系在图形对象表示与识别中的应用

工程图纸识别中,图形对象的表示方法是研究的重点和难点。在矢量化的基础上提出了一种基于拓扑结构的图形对象表示与识别方法,该方法通过矢量化获得各个图形对象的矢量基元,通过建立矢量基元的影响区域确定各矢量基元的拓扑邻接关系图,进而确定图形对象之间的位置关系,为进一步理解识别工程图纸提供了基本的信息。然后介绍了一个应用实例：圆弧角的识别。在实验中,分别用标准数据和真实图纸进行了测试,实验结果显示该算法具有较高的识别精度和识别效率。     

智能装配机器人机械零件知识库的自动建立

为了实现智能装配机器人能够模拟人装配机械零件,系统应包含有关装配所必需的知识.利用这些知识,机器人自动编制装配工艺规程.并自动安排机器人的一系列控制命令.机械零件知识库是基于知识的智能装配机器人规划系统中不可缺少的知识.本文介绍了由机器人的视觉系统摄入机械零件图纸后,如何由摄入的二值化图形自动建立机械零件知识库.     

A knowledge-based system for extracting text-lines from mixed and overlapping text/graphics compound document images

This paper presents a new knowledge-based system for extracting and identifying text-lines from various real-life mixed text/graphics compound document images. The proposed system first decomposes the document image into distinct object planes to separate homogeneous objects, including textual regions of interest, non-text objects such as graphics and pictures, and background textures. A knowledge-based text extraction and identification method obtains the text-lines with different characteristics in each plane. The proposed system offers high flexibility and expandability by merely updating new rules to cope with various types of real-life complex document images. Experimental and comparative results prove the effectiveness of the proposed knowledge-based system and its advantages in extracting text-lines with a large variety of illumination levels, sizes, and font styles from various types of mixed and overlapping text/graphics complex compound document images.     

Concurrent intelligent rapid prototyping environment

Rapid prototyping technology enables rapid production of complex objects directly from a computer-aided design model without involving any tooling or conventional part programming. This has created a new set of problems associated with part design, process planning, support design and value engineering analysis of rapid prototyping parts. In this paper, a methodology for resolving these problems is described, which uses concurrent engineering, distributed blackboard, value engineering, knowledge-based and feature-based technologies. The functionality, design methodology and knowledge representation techniques of a concurrent intelligent rapid prototyping system for stereolithography form the main focus of this paper.     

USING REENGINEERING FOR KNOWLEDGE-BASED SYSTEMS

Reverse engineering, also called reengineering, is used to modify systems that have functioned for many years, but which can no longer accomplish their intended tasks and, therefore, need to be updated. Reverse engineering can support the modification and extension of the knowledge in an already existing system. However, this can be an intricate task for a large, complex and poorly documented knowledge-based system. The rules in the knowledge base must be gathered, analyzed and understood, but also checked for verification and validation. We introduce an approach that uses reverse engineering for the knowledge in knowledge-based systems. The knowledge is encapsulated in rules, facts and conclusions, and in the relationships between them. Reverse engineering also collects functionality and source code. The outcome of reverse engineering is a model of the knowledge base, the functionality and the source code connected to the rules. These models are presented in diagrams using a graphic representation similar to Unified Modeling Language and employing ontology. Ontology is applied on top of rules, facts and relationships. From the diagrams, test cases are generated during the reverse engineering process and adopted to verify and validate the system.     

An object-oriented progressive-simplification-based vectorization system for engineering drawings: model, algorithm, and performance

Existing vectorization systems for engineering drawings usually take a two-phase workflow: convert a raster image to raw vectors and recognize graphic objects from the raw vectors. The first phase usually separates aground truth graphic object that intersects or touches other graphic objects into several parts, thus, the second phase faces the difficulty of searching for and merging raw vectors belonging to the same object. These operations slow down vectorization and degrade the recognition quality. Imitating the way humans read engineering drawings, we propose an efficient one-phase object-oriented vectorization model that recognizes each class of graphic objects from their natural characteristics. Each ground truth graphic object is recognized directly in its entirety at the pixel level. The raster image is progressively simplified by erasing recognized graphic objects to eliminate their interference with subsequent recognition. To evaluate the performance of the proposed model, we present experimental results on real-life drawings and quantitative analysis using third party protocols. The evaluation results show significant improvement in speed and recognition rate.     

工程图学内涵的变化与发展

工程图学是研究工程图样表达与技术交流的一门学科。图形计算及数字化制造等 技术的飞快发展,正在改变着工程图的呈现介质、应用范围、交流主体、甚至工程图本身的定 义。这些变化正在刷新着工程图学的内涵,同时,也正在重塑人们对工程表达与交流的认知。 为此,本文力图挖掘工程图学在变化中的共性基础理论,分析其变化中的发展规律与趋势,并 讨论数字化制造背景下的工程图学的发展定位以及与发展相适应的工程图学知识体系。最后给 出与此相适应的教学定位、教材规划以及教学模式。     

工程图纸智能识别的分层理解模型

从工程图（机械工程图）的二重特性出发，分析了人脑识图的方法和特点，并模拟人脑识图过程，提出了基于知识的工程图智能识别的分层理解模型。分析了其总体结构、控制机制以及识图知识的利用。     

An interactive example-driven approach to graphics recognition in engineering drawings

An interactive example-driven approach to graphics recognition in engineering drawings is proposed. The scenario is that the user first interactively provides an example of a graphic object; the system instantly learns its graphical knowledge and uses the acquired knowledge to recognize the same type of graphic objects. The proposed approach represents the graphical knowledge of an object in terms of its structural components and their syntactical relationships. We summarize four types of geometric constraints for knowledge representation, based on which we develop an algorithm for knowledge acquisition. Another algorithm for graphics recognition using the acquired graphical knowledge is also proposed, which is actually a sequential examination of these constraints. In the algorithm, we first guess the next component’s attributes (e.g., size, position and orientation) by reasoning from an earlier found component and the constraint between them, and then search for this hypothetical component in the drawing. If all of the hypothetical components are found, a graphic object of this type is recognized. For improving the system’s recognition accuracy, we develop a user feedback scheme, which can update the graphical knowledge from both positive (missing) and negative (mis-recognized) examples provided by the user for subsequent recognition. Experiments have shown that our proposed approach is both efficient and effective for recognizing various types of graphic objects in engineering drawings. This paper is an extension of our papers published in ICDAR2003 and GREC2003.     

A RFBSE model for capturing engineers’ useful knowledge and experience during the design process

Reuse of designers’ knowledge and experience of solving problems during the engineering design process holds the key to increase efficiency of decision making in future projects. An important part of this useful knowledge and experience is the interpretation of data and information about design objects and processes as well as the generation of new information for decision-making. However, previous studies on knowledge representation models have mainly focused on developing a structure to describe the knowledge about design objects and design processes while a systematic method that can effectively integrate knowledge about design objects and knowledge about problem-solving strategies is still missing. To fill this gap, a RFBSE knowledge representation model for capturing useful design knowledge and experience for future reuse is developed and evaluated in this study. This paper describes the key elements of this model, explains the rationale of using particular elements, and discusses the evaluation of the model using an engineering design example.     

Simulation and knowledge based objects

A knowledge-based simulation model was developed by using a hypertext-like semi-object-oriented environment (SuperCard^TM). This environment enabled the building of a tightly coupled knowledge representation scheme and simulation algorithm. Our knowledge-based simulation model combines three different paradigms: production rules, an inference engine, and knowledge-based objects. The production rule paradigm is used as the basis for describing and coding the processes to be simulated. The knowledge-based objects are built as the active objects of the simulation. A knowledge-based object has a number of rules that support its behavior and an agenda that defines its goals. A knowledge-based object then proceeds in the simulation process (via the inference engine) until its goals are achieved. Since an inference engine with a knowledge base is built into the simulation process, such a system has the ability to reason about the behavior of the model. In addition, the knowledge-based simulation model has a friendly user interface. This enables changing the parameters of the simulation model without doing any programming. The inference engine in the knowledge-based simulation combines backward chaining, decision lattices, and links so as to produce an overall pattern of inference that is as efficient as possible.     

KIMS—A knowledge-based computer vision system for production line inspection

the aim of this paper is to discuss a generic expert system prototyped for image analysis and interpretation tasks. The system christened KIMS is a knowledge-based image management system which oversees the entire process of image processing, segmentation, feature extraction, knowledge representation along with an expandable capability for image understanding task. We discuss the architecture of KIMS, its modeling environment, and demonstrate its usefulness with photo-to-image inspection as applied to a manufacturing line.     

Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution

Answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods/tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory.