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Volume 41 Issue 10
Oct.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(10): 1935-1942.
DENG Wei, WU Lingda, ZHANG Yougen, et al. Online sketch recognition for mixed point and irregular military symbols[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1935-1942. doi: 10.13700/j.bh.1001-5965.2014.0826(in Chinese)
Citation: DENG Wei, WU Lingda, ZHANG Yougen, et al. Online sketch recognition for mixed point and irregular military symbols[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1935-1942. doi: 10.13700/j.bh.1001-5965.2014.0826(in Chinese)
shu

Online sketch recognition for mixed point and irregular military symbols

doi: 10.13700/j.bh.1001-5965.2014.0826
  • 1.

    Science and Technology on Complex Electronic System Simulation Laboratory, Academy of Equipment, Beijing 101416, China

  • 2. Department of Information Systems, Academy of National Defense Information, Wuhan 430010, China

  • 3. Institute of Information Technology of Guilin, Guilin 541004, China

  • Received Date: 30 Dec 2014
  • Rev Recd Date: 10 Apr 2015
  • Publish Date: 20 Oct 2015
    Abstract
  • Most of current research on online sketched military symbols recognition concerns only one type of symbols, point symbols or irregular symbols, using different methods to recognize separately. But in practical applications the two types of symbols are mixed. It becomes a major issue to find a way to recognize a type-unknown military symbol. A minimum spanning tree (MST) covering model-based mixed recognition method was proposed. In the training phase, two MST-based covering models were built for point and irregular symbols respectively. And then a two-class support vector machine (SVM) classifier was trained. In the recognition phase, the coarse type identification was accomplished by using the geometrical and structural information firstly. Then the confidence estimations were calculated and integrated to identify the type of the unknown symbol. Different types of symbols were classified by two existing modules. The algorithm was tested on 113 classes of point symbols and 36 classes of irregular symbols. The accuracy rate of symbol type identification was 94.7%, and the final recognition rate was 91.6% in real time.

     

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