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  • Cybernetics, Artificial Intelligence and Robotics  (1)
  • Key words:3D Drawing interpretation – Document image analysis – Graphics recognition – Image processing – Engineering drawings  (1)
  • construction  (1)
  • 1
    Keywords: Dokument-Bildanalyse - Engineering Drawings - Erkennungsalgorithmen - Graphics Recognition - Ingenieurzeichnungen ; Landkarteninterpretation ; Map Interpretation ; Recognition Algorithms ; algorithms ; cognition ; construction ; knowledge ; learning ; model ; verificat
    Description / Table of Contents: This book contains revised refereed papers selected from the presentations at the First International Workshop on Graphics Recognition, held in University Park, PA, USA, in August 1995. The 23 full papers included are divided into sections on low-level processing, vectorization and segmentation of scanned graphics documents; symbol and diagram recognition, map processing, interpretation of engineering drawings. Each section contains both survey articles to assess the state of the art, and research papers presenting novel results. One section is devoted to a contest held to determine the best algorithm for detection of dashed lines in drawings. The final chapter summarizes the conclusions and recommendations of the discussions held during the workshop.
    Pages: Online-Ressource (X, 314 pages)
    ISBN: 9783540683872
    Language: English
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    International journal on document analysis and recognition 2 (1999), S. 120-131 
    ISSN: 1433-2825
    Keywords: Key words:3D Drawing interpretation – Document image analysis – Graphics recognition – Image processing – Engineering drawings
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. 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.
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2019-07-13
    Description: In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Format: application/pdf
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