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Open AccessArticle10.2991/eame-15.2015.70

Novel Method for Real-Time Moiré Image Analysis Combining Two-Dimensional Entropy Theory and Quantum-Behaved Particle Swarm Optimization

W.J. Chen,Y.T. Chen-2015-01-01-Advances in engineering research/Advances in Engineering Research
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TL;DRAbstract

This paper proposes an effective method for improving the processing quality and speed of Moir pattern segmentation. The method involves using quantum-behaved particle swarm optimization(QPSO) based on two-dimensional (2D) entropy theory.First, the beat phenomena generated by grating interference, also called a Moir pattern fringe, areextracted through FFT filtering. Subsequently, the fringe is segmented by two thresholds with maximized 2D entropy based on a QPSO algorithm. Verifying the experimental results showed that the proposed approach enabled obtaining an improved segmentation quality, fast computing performance, and favorable convergent effects.

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This paper proposes an effective method for improving the processing quality and speed of Moir pattern segmentation. The method involves using quantum-behaved particle swarm optimization(QPSO) based on two-dimensional (2D) entropy theory.First, the beat phenomena generated by grating interference, also called a Moir pattern fringe, areextracted through FFT filtering. Subsequently, the fringe is segmented by two thresholds with maximized 2D entropy based on a QPSO algorithm. Verifying the experimental results showed that the proposed approach enabled obtaining an improved segmentation quality, fast computing performance, and favorable convergent effects.

Keywords

Particle swarm optimizationMoiré patternEntropy (arrow of time)Computer scienceAlgorithmSwarm behaviourQuantumStatistical physics

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