• DocumentCode
    2956910
  • Title

    Simulated annealing for hierarchical pattern detection and seismic applications

  • Author

    Huang, Kou-Yuan ; Chou, Ying-Liang

  • Author_Institution
    Dept. of Comput. Sci., Nat. Chian Tung Univ., Hsinchu
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    1257
  • Lastpage
    1264
  • Abstract
    A hierarchical system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures are type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Also we use the minimum error to determine the number of patterns. The proposed simulated annealing parameter detection system can search a set of parameter vectors for the global minimal error. In the experiments, using the hierarchical system, the result of the detection of a large number of simulated image patterns is better than that of using the synchronous system. In the seismic experiments, both of two systems can well detect line of direct wave and hyperbola of reflection wave in the simulated one-shot seismogram and the real seismic data, but the hierarchical system can converge faster. The results of seismic pattern detection can improve seismic interpretation and further seismic data processing.
  • Keywords
    geophysical signal processing; seismology; simulated annealing; global minimal error; hierarchical pattern detection; image hyperbola; parameter vectors; reflection wave hyperbola; seismic applications; seismic data processing; seismic pattern detection; simulated annealing parameter detection system; Neural networks; Simulated annealing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4633960
  • Filename
    4633960