• International Journal of Precision Engineering and Manufacturing
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• v.5 no.3
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• pp.11-18
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• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.477-480
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• 2005

In this paper, we propose a preprocess method to needs for Car License Plate Recognition on poor environment. This preprocess method use multi images to get low value to compare images value. Last method was Opening operation that Using Edge pixel to add and subtraction. The Result was removed White pixel and very mini feather. But This method needs many process times and License Plate Recognition is low quality problem. Another method is median filter and conversion. This paper key idea that rain & snow is high value. So This paper propose get low value to compare image value.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.1
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• pp.1-6
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• 2004

In this paper, we present the new method for joint aspect inspecting system. We use the image processing and laser maker for light source. We can find the matrial joint status through processing the line pattern which is made by laser maker. To get the line pattern, in first, we did the preprocess of threshold. If the shape of line had over two segments, then the joint status is abnormal. We show our system efficency by experiment on tire facility.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.637-640
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• 1999

In this Paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. The proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.6 respectively.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.969-970
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• 2006

This paper develops a recognition system of assembly parts using a hardware independent image module. Using a shared memory, the image module consists of the image acquiring process and the image processing process. We preprocess an acquisition image from the module, approximate the image edges to an ellipse, and then recognize an assembly part by matching the ellipse to a model base one.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.38-45
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• 2007

We propose a preprocessing method for removing floating particles from underwater images based on an analysis of the image features. We compared baseline JPEG and wavelet codec methods to determine the method best suited for underwater images. The proposed preprocessing method enhanced the compression ratio and resolution, and provided an efficient means of compressing the images. The wavelet codec method yielded better compression ratios and image resolutions. The results suggest that the wavelet codec method linked with the proposed preprocess method provides an efficient codec processor and transmission system for underwater images that are used for searches and transmitted via ultrasonic communications.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1472-1475
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• 1999

This paper proposes a new detection method of human faces in grey scale images with cluttered background using a facial template and elliptical structure of the human head. Face detection technique can be applied in many areas of image processing such as face recognition, composition and computer graphics, etc. Until now, many researches about face detection have been done, and applications in more complicated conditions are increasing. The existing technique proposed by Sirohey shows relatively good performance in image with cluttered background, but can apply only to image with one face and needs much computation time. The proposed method is designed to reduce complexity and be applied even in the image with several faces by introducing template matching as preprocess. The results show that the proposed method produces more correct detection rate and needs less computation time than the existing one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.687-701
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• 2020

In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

• Journal of IKEEE
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• v.19 no.2
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• pp.148-154
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• 2015

Automotive electronics system must alarm accurately in every moment. In order to apply vehicle's image recognition algorithms, it is necessary to preprocess the system quickly. In this paper, blurred image correction method that utilizes histogram equalization and bilateral filter using deviation for driver assist system's image processing is proposed. It forms 5-stage processes namely scaler, equalization, modified noise filter, blur decision and edge detector. Using the extracted proper, values in bilateral filter for driving environment occurred driver assist system, the proposed algorithm is much faster processing time compare to the previous methods in blurred within 10 pixel. Results of experiment which are run time and experimental PSNR results using MATLAB is obtained and verified that our proposed algorithm is more faster performance compare with the existing methods.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.79-83
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• 2021

This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.