• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1282-1287
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• 2005

The accuracy of chip alignment in inferior product inspection of COG(Chip On Glass) to be measured a few micro unit is very important role since the accuracy of chip inspection depends on chip alignment. In this paper, we propose the area segmentation pattern matching method to enhance the accuracy of chip alignment. The area segmentation pattern matching method compares, and matches correlation coefficients between the characteristic features within the detailed area and the areas. The three areas of pattern circumference are learned to minimize the matching error by bad pattern. The proposed method has advantage such as reduction of matching time, and enhanced accuracy since the characteristic features are searched within the segmented area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.308-314
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• 2006

A line scan camera is applied to enhance COG(Chip On Glass) inspection accuracy to be measured a few micro unit. The foreign substance detection among various faulty factors has been the most difficult technology in the faulty automatic inspection step since COG pattern is very miniature and complexity. In this paper, we proposed two step area segmentation template matching method to increase matching speed. Futhermore to detect foreign substance(such as dust, scratch) with a few micro unit, the new method using gradient mask and AND operation was proposed. The proposed 2 step template matching method increased 0.3 - 0.4 second matching speed compared with conventional correlation coefficient. Also, the proposed foreign substance applied masks enhanced $5-8\%$ faulty detection rate compared with conventional no mask application method.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.203-208
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• 2005

The CGO (Chip on Glass) to be measured a few micro unit is captured by line scan camera for the accuracy of chip inspection. But it is very sensitive to scan speed and lighting conditions. In this paper, we propose the methods to increase the accuracy of faulty detection by image subtraction. Image subtraction is detected faultiness by subtracting the image of a ' perfect ' COG from trot of the sample under tests. For image subtraction to be successful, the two images must be pre챠sely registered The two images is registered by the area segmentation pattern matching, and the result image get by operating the gradient mask image and the image to practice subtraction. A series of experimentation showed that the proposed algorithm shows substantial improvement over the other image subtraction methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.63-70
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• 2014

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate.