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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.

By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.

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.

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 accurate results with a low‐error rate.

In terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection.

The paper describes how pre‐inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi‐defects to inspect and improve six defects simultaneously, which improves the system stability greatly.

The purpose of this paper is to propose an automatic pattern matching template generating method for the automatic optical inspection system in TFT LCD assembly and positioning process, to improve the conventional image technology. Besides, focusing on integrating the image system with the existing control system, the double aligner mark searching time is decreased to reduce the working time of the integrated system.

The improved pattern matching method of genetic algorithm was adopted, including setting for template image selecting, encoding, calculating fitness function, pattern matching, template generating and genetic algorithm steps. The predetermined pixels were selected from the target template based on the minimum difference to the block image to be tested by utilizing the genetic algorithm, and the other pixels which have not been selected were neglected.

The selected pixels were encoded for recording by sequence mode, and then the target template and the image to be tested were compared based on the calculated fitness function. This method has the advantages of using the fitness function to reduce the searching time, with the help of genetic algorithm to find the optimal target template, and saving memory space by recording target template based on the sequence mode.

The genetic algorithm used in this study is a kind of optimal tool free from gradient data. As long as the fitness function and after continuous iteration are determined, the optimal solution can be found out, and then the optimal target template can be generated.

This system uses fitness function to reduce the pattern matching time. Plural pixels are preset inside the target template, and its fitness function value is calculated. When the target template is compared with the image to be tested, only the fitness function value (also the difference of the plural pixels) is calculated and compared.

The remaining pixels are neglected, so that the searching time can be reduced greatly. The sequence mode is used to save the required memory space for recording target template. Since sequence mode is adopted to record the information of selected pixels, lots of required memory space for recording target template information will be saved.

The purpose of this research is to develop an automatic optical inspection system for thin film transistor (TFT) liquid crystal display (LCD).

A new algorithm that accounts for the closing, opening, etching, dilating, and genetic method is used. It helps to calculate the location and rotation angle for transistor patterns precisely and quickly. The system can adjust inspection platform parameters according to viewed performance. The parameter adaptation occurs in parallel with running the genetic algorithm and imaging processing methods. The proposed method is compared with the algorithms that use artificial parameter sets.

This system ensures high quality in an LCD production line. This multipurpose image‐based measurement method uses unsophisticated and economical equipment, and it also detects defects in the micro‐fabrication process.

The experiment's results show that the proposed method offers advantages over other competing methods.

The purpose of this paper is to apply what can be learned from the emergence of nature tourism to understand some current and future trends of tourism.

This study adopted the evolutionary paradigm for investigation.

The emergence of nature tourism in early medieval China can be attributed to four major factors, including transformation of value orientations, seeking longevity, interest in suburbs and population migration.

Historical studies help understand the current and future trends. When the contributing factors for nature tourism are linked to the contemporary world, it can be found that these factors are still playing a part in shaping tourism trends or patterns in their original or alternative forms. These trends or patterns are worthy of scholarly investigations.

This paper offers a comprehensive understanding of the origins of nature tourism.