A relative frequency-based independent component analysis model for defect detection and motion detection

• Main Authors: Shia-Chih Lai, 賴夏枝
• Other Authors: 蔡篤銘
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/98018610711431514165

碩士 === 元智大學 === 工業工程與管理學系 === 94 === In this study, an independent component analysis (ICA) model that directly measures the difference of the joint probability density function (p.d.f.) and the product of marginal p.d.fs is proposed. The p.d.fs are estimated from the relative frequency distributions, and the particle Swarm Optimization (PSO) algorithm is used to search for the best solution in the ICA model. The proposed ICA model can separate highly correlated data, which is not achievable by the well-known FastICA algorithm that uses non-gaussianity as the independency measure. Since the p.d.f. estimated in the ICA model is simply based on the count of relative frequency, it can only well separate mixture of two source signals. When it comes to more than two signals, the p.d.f. estimation performs unreliably. Therefore, the applications of the proposed ICA model in this research are restricted to the separation of two sources. The proposed ICA model is applied to defect detection and motion detection, where the underlying signals show high correlation. For defect detection, panel surfaces of TFT-LCD and color filter are the main targets of study. In a panel image, each scan line shows a periodical pattern. By dividing a scan line into two segments of equal length, the two segments are only different by their translations. The proposed ICA model is applied to filter translation changes. A cross correlation-based similarity measure can then be used to identify anomalies in the inspection surface. For motion detection, the proposed ICA model is applied to separate foreground objects from the stationary background. The implementation of the proposed method is computationally fast, and is insensitive to illumination changes. Experimental results have shown that the proposed methods are very efficient and effective for defect detection and motion detection applications.