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A Fast Quantum Image Component Labeling Algorithm

Author

Listed:
  • Yan Li

    (School of Business Administration, Xi’an Eurasia University, Xi’an 710065, China)

  • Dapeng Hao

    (School of Science, Xi’an Aeronautical University, Xi’an 710077, China)

  • Yang Xu

    (School of Economics and Management, Xi’an Technological University, Xi’an 710021, China)

  • Kinkeung Lai

    (Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Hong Kong 999077, China)

Abstract

Component Labeling, as a fundamental preprocessing task in image understanding and pattern recognition, is an indispensable task in digital image processing. It has been proved that it is one of the most time-consuming tasks within pattern recognition. In this paper, a fast quantum image component labeling algorithm is proposed, which is the quantum counterpart of classical local-operator technique. A binary image is represented by the modified novel enhanced quantum image representation (NEQR) and a quantum parallel-shrink operator and quantum propagate operator are executed in succession, to finally obtain the component label. The time complexity of the proposed quantum image component labeling algorithm is O ( n 2 ) , and the spatial complexity of the quantum circuits designed is O ( c n ) . Simulation verifies the correctness of results.

Suggested Citation

  • Yan Li & Dapeng Hao & Yang Xu & Kinkeung Lai, 2022. "A Fast Quantum Image Component Labeling Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2718-:d:877839
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    Cited by:

    1. Fernando L. Pelayo & Mauro Mezzini, 2022. "Preface to the Special Issue on “Quantum Computing Algorithms and Computational Complexity”," Mathematics, MDPI, vol. 10(21), pages 1-3, October.

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