Hung-Jung Shei

An Automatic Inspection Method for the Fracture Conditions of Anisotropic Conductive Film in the TFT-LCD Assembly Process

In this study an automatic optical inspection system is presented to evaluate the fracture and deformation status of conducting particles of anisotropic conductive film in the TFT-LCD assembly process. The amount of deformation and quantity of conducting particles in the test pattern can be automatically evaluated by image analysis. A specific operation is carried out in the image processing method, and the calculation of the image gradient operator is used to produce a preferable contrast between the processed particle image and the background. The thinning processing method is applied for information reduction and information creation. An amount of samples are taken with a target template for synchronous multiple-comparison, and the optimal threshold of the binary image is obtained. This study utilizes the assistance of image processing technology to inspect the fracture conditions of anisotropic conductive film in the TFT-LCD assembly process. This system can decrease the defection rate of products, obtain over 90% recognition accuracy even in noisy environments, and will be verified in an automatic production line.

Automatic inspection and strategy for surface defects in the PI coating process of TFT‐LCD panels

Purpose – The purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.Design/methodology/approach – By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.Findings – With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.Research limitations/implications – The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accu...

An automatic inspection and control method for a biological reagent production system with image processing techniques

Purpose – The purpose of this paper is to present an automatic inspection and control method for a reagent rapid test strip production system, with image processing techniques.Design/methodology/approach – Fluorescence, color arrangement and combination matching with the database were used to identify the responses of biochemicals. The position accuracy and insufflation consistency between the control line and test line on a reagent rapid test strip will be analyzed from the image after series processing.Findings – The system can identify failed products and regulate production conditions to insure that the quality standard is maintained. The idea edges of the control line and test line are the boundary at which a significant change occurs in the surface reflectance and illumination of the viewer. But the change of the real boundary of the test line may be insufficient for identification.Research limitations/implications – As the illumination of biological reagent images cannot be measured precisely in th...

A control interface and imaging system for eyeball features measurement

In this study, two white light LEDs were mounted on the upper and lower parts of a CCD camera. By using imaging and lighting control programs, lighting signals were transferred to a monolithic chip light control circuit, so that two white light LEDs could generate instantaneous flash within different preset time intervals. After capturing the images of two flash instants using the CCD camera, the center of the pupil is taken as the center of a circle to measure the rounded edge. The second image is captured when the brightness of the second pulsed light is equivalent to that of the first pulsed light, and the pupil size is the same as that of the first image. The iris region is searched from outside to inside by simply calculating the objective function value at discretely sampled radius and center. The captured images are classified as ‘zone of rejection’, ‘reserved zone’ and ‘merge zone’. A new interpolation method is used for merging two images to complete an image without any reflective dot, as well as a safe lighting design, and double-pulse iris imaging system.