• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.280-282
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• 2009

본 논문에서는 굴곡에 의한 조도량의 차이와 명암도 차이를 퍼지 기법에 적용하여 개선된 반도체 불량 검출 방법을 제안한다. 제안된 방법은 먼저 회전각과 양선형 보관법을 이용하여 반도체 영상의 각도를 보정하는 전처리 과정 수행한다. 그리고 굴곡에 대한 조도량의 차이와 패턴 매칭를 이용하여 얻어진 오류 영역의 명암도 차이를 퍼지 소속 함수에 적용하여 결과 값을 추론한다. 최종적으로 비퍼지화된 결과 값을 적용하여 반도체의 초기 불량을 검출한다. 본 논문에서 제안한 방법을 실제 사용되는 반도체 정면 영상과 측면 영상 30쌍을 대상으로 실험한 결과, 기존의 방법에 비해서 반도체의 초기 불량 판단에 효과적인 것을 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.19-21
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• 2019

본 논문에서는 인공지능의 인식률을 높이기 위해 명암비를 적용한 전처리 방법에 대해 제안한다. 이 방법은 인공지능이 기존의 방법보다 특징점을 쉽게 얻어내기 위해서 명암비를 적용한 전처리를 진행하여 인식률을 높이기 위함에 목적을 두고 있다. 제안한 방법으로 인하여 문자인식이 얼마나 향상되었고, 기존의 성능과 비교하여 정확도가 얼마나 향상되었는지를 알아보고 있다.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.599-610
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• 2010

Color laser printers are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use their own printing process, each of printed papers from different printers has a little different invisible noise. After the wiener-filter is used to analyze the invisible noises from each printer, we extract some features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, we use total 2,597 images from 7 color laser printers. The results prove that the presented identification method performs well using the noise features of color printed images.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1115-1127
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• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2121-2127
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• 2017

In this paper, we propose a computer vision based automatic defect detection method from ceramic image for non-destructive testing. From region of interest of the image, we apply brightness enhancing stretching algorithm first. One of the strength of our method is that it is designed to detect defects of images obtained from various thicknesses, that is, 8, 10, 11, 16, and 22 mm. In other cases we apply histogram based binarization algorithm. However, for 8 mm case, it may have false positive cases due to weak brightness contrast between defect and noise. Thus, we apply modified fuzzy binarization algorithm for 8 mm case. From the experiment, we verify that the proposed method shows stronger result than our previous study that used Blob labelling for all five thickness cases as expected.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.149-156
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• 2003

This paper proposes a face recognition method based on modified Otsu binarization, Hu moment and linear discriminant analysis (LDA). Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. Modified Otsu binarization can make binary images that have the invariant characteristic in brightness and contrast changes. From edge and multi-level binary images obtained by the threshold method, we compute the 17 dimensional Hu moment and then extract feature vector using LDA algorithm. Especially, our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. Experimental results showed that our method had almost a superior performance compared with the conventional well-known principal component analysis (PCA) and the method combined PCA and LDA in the perspective of brightness, contrast, scale, rotation, and translation changes with Olivetti Research Laboratory (ORL) database and the AR database.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.30-35
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• 2010

This paper presents a quality enhancement of images by using a histogram equalization based on the symmetric logistic function. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a sigmoidal nonlinear transformation function, is applied to non-linearly enhance the brightness of the image according to its intensity level frequency. We propose a flexible and symmetrical logistic function by only using the intensity with maximum frequency in an histogram and the total number of pixels. The proposed function decreases the computation load of an exponential function in the traditional logistic function. The proposed method has been applied for equalizing 5 images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances compared with the source images and the traditional global histogram equalization, respectively.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.113-121
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• 2015

Artifact pattern representation of a method using the digital technology is largely are two digital rubbing technique and digital contrast technique. Digital rubbing technique is clearly with the naked eye to see the original prestigious and difficult to build a digital rubbing data patterns by scanning a prestigious and patterns carved into the artifacts in three dimensions without compromising the circle to represent the three-dimensional effect by contrast it to the objective value refers to a technique that can be represented in digital. Digital contrast technique can not be calculated if the depth information of the object due to the glassy film of artefacts in a three-dimensional surface scanning and the application of this technique refers to a realistic image mapped digital technique for generating digital image contrast. Digital rubbing technique is mainly useful to express the most prestigious and patterns expressed in pottery or tile and stone monument, etc. engraved or embossed, digital contrast techniques can be used effectively to artifacts such as magnetic or glass can not be applied to the digital rubbing technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.787-795
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• 2009

This paper presents a histogram equalization based on the logistic function for enhancing the quality of images. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a nonlinear transformation function is applied to adaptively enhance the brightness of the image according to its intensity level frequency. We propose a flexible and asymmetrical logistic function by only using the intensity level with maximum frequency and the maximum intensity level in an histogram, and the total number of pixels. The proposed function excludes both the computation load of an exponential function and the heuristic setting of an optimal parameter values in the traditional logistic function. The proposed method has been applied for equalizing many images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances and the faster equalizing speed compared with the traditional histogram equalization and the adaptively modified histogram equalization, respectively. And the proposed histogram equalization can be used in various multimedia systems in real-time.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.7-14
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• 2013

As mobiles devices with high-spec camera built in are used widely, the visual quality enhancement of the high-resolution images turns out to be one of the key capabilities of the mobile devices. Due to the limited computational resources of the mobile devices and the size of the high-resolution images, we should choose an image processing algorithm not too complex and use an efficient implementation technology. One of the simple and widely used image quality enhancement algorithms is contrast stretching. Java libraries running on a virtual machine, JNI (Java Native Interface) based native C/C++, and NEONTM SIMD (Single Instruction Multiple Data) are common implementation technologies in the case of Android smartphones. Using these three implementation technologies, we have implemented two image contrast stretching algorithms - linear and equalized, and compared the computation times. The native C/C++ and the NEONTM SIMD outperformed the native C/C++ implementation by 56-78 and 50-76 time faster respectively.