Automatic Optical Inspection Techniques Based on Non-uniform Background Fitting

• Main Authors: You-Ching Lee, 李侑青
• Other Authors: Din-Chang Tseng
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/97599602036063089551

博士 === 國立中央大學 === 資訊工程學系 === 101 === In recent years, object detection has become more popular for industry applications due to the usage of advanced scanning devices and the requirement of visual inspector. Moreover, due to the growth of image and video data, many issues of automatic object detection are expected such as substitution for human inspection, acceleration of inspection speed, and increases on inspection correctness. These issues include biomedical image diagnosis, deoxyribonucleic acid (DNA) electrophoresis analysis, protein electrophoresis analysis, vehicle safety monitoring, solar cell production inspection, semi-conductor wafer inspection, thin-film-transistor liquid-crystal display (TFT-LCD) inspection, texture segmentation, human face detection, house surveillance, etc. In this dissertation, we’ll discuss three of these interesting auto-detection issues: DNA electrophoresis analysis, TFT-LCD inspection, and texture segmentation. Among the three issues, foreground objects appear on non-uniform background. For DNA electrophoresis analysis, backgrounds are fitted by one dimensional curves; for TFT-LCD inspection, backgrounds are fitted by two dimensional planes; for texture segmentation, Gabor magnitudes of texture backgrounds are fitted by hyper planes. Before background fitting, each issue needs some pre-processing which is suitable for the image features of each issue. In DNA electrophoresis analysis, we proposed a completely automatic band detection system for pulsed-field gel electrophoresis (PFGE) images. Band detection comprises lane segmentation and band assignment. The lane segmentation algorithm characterizes features of the PFGE images and uses optimal line fitting to separate lanes. The band assignment algorithm uses polynomial fitting to remove the uneven background and uses gradient features of bands to detect bands. In TFT-LCD inspection, we proposed an online TFT-LCD mura defect detection method which consists of illumination calibration, multi-image accumulation, and multi-resolution background subtraction. First, an LCD on a moving product conveyer is contiguously captured by several images with different locations and a synthesized LCD image is used to calibrate the non-uniform illumination of the images. Second, the images are aligned in position to accumulate the gray levels of pixels which all correspond to a point on the LCD. Third, the multi-resolution backgrounds of the accumulated image are progressively estimated based on the discrete wavelet transform (DWT). We take the accumulated image into a multi-resolution and then refine the estimated background from coarse to fine. The accumulated image subtracted from the estimated background leaves the defect candidates. Finally, a standard thresholding method is used to “threshold out” the mura defects. In texture segmentation, we proposed an unsupervised texture segmentation method using optimal asymmetric Gabor filter (AGF) based on active contour model. First, we create a formula of the asymmetric Gaussian function and multiply a two dimensional (2D) complex sinusoidal function to the function to construct a 2D AGF. Then, compute the average and the variation of the Gabor magnitudes to capture the probability distribution of the Gabor magnitudes. The average and variation are used in the level-set energy functional to evolve the level-set contour. To obtain an AGF which is optimal to the current evolution contour, we propose a Fisher-like function which determines the optimal AGF for the processed image at every iteration determined. Finally, the proposed algorithm of active contour is described. Experiments demonstrate the proposed automatic object detection techniques: band detection system, mura detection method, and unsupervised texture segmentation. The band detection system can automatically segment the lanes in the gel images and detect the bands in the lanes. The band detection rate is 98.42%. The mura detection method can detect mura defects with arbitrary directions, shapes, and sizes. The detection rate of mura regions is 100%. The proposed unsupervised texture segmentation method can distinguish two different textural regions without pre-selecting a suitable Gabor filter.