• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.444-449
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• 1996

The semiconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip affect on the functions of the semiconductors. The defects of the chip surface are cracks or voids. As general inspection method requires many inspection procedure, the inspection system which searches immediately and precisely the defects of the semiconductor chip surface is required. We suggest the detection algorithm for inspecting the surface defects of the semiconductor surface. The proposed algorithm first regards the semiconductor surface as random texture and point spread function, and secondly presents the character of texture by linear estimation theorem. This paper assumes that the gray level of each pixel of an image is estimated from a weighted sum of gray levels of its neighbor pixels by linear estimation theorem. The weight coefficients are determined so that the mean square error is minimized. The obtained estimation window(two-dimensional estimation window) characterizes the surface texture of semiconductor and is used to discriminate the defects of semiconductor surface.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.100-106
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• 2011

Music recordings have been digitalized such that huge size of music database is available to the public. Thus, the automatic classification system of music genres is required to effectively manage the growing music database. Mel-Frequency Cepstral Coefficient (MFCC) is a popular feature vector for genre classification. In this paper, the combined super-vector with Decorrelated Filter Bank (DFB) and Octave-based Spectral Contrast (OSC) using texture windows is processed by Support Vector Machine (SVM) for genre classification. Even with the lower order of the feature vector, the proposed super-vector produces 4.2 % improved classification accuracy compared with the conventional Marsyas system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Progress in Superconductivity and Cryogenics
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• v.5 no.3
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• pp.11-14
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• 2003

The detailed conditions of dc-sputtering for depositions of yttria-stabilized ZrO$_2$ (YSZ) films were investigated, while the films were grown on the CeO$_2$ template layers on biaxially textured Ni-tapes. The window of oxygen pressures for proper growth of YSZ films, which was dependent on sputtering powers, was determined by sufficient oxidations of the YSZ films and the de-oxidation of the target surface, which was required for rapid sputtering. The window turned out to be fairly wide under certain values of argon pressure. When the sputtering power was raised, the deposition rate increased without narrowing the window. The fabricated YSZ films showed good texture qualities and surface morphologies.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.799-802
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• 2000

There are many methods that extract the depth information based on the blurring ratio for object point in DFD(Depth from Defocus). However, it is often difficult to measure the depth of the object in two-dimensional images that was affected by various elements such as edges, textures, and etc. To solve the problem, new DFD method employing the texture classification with a neural network is proposed. This method extracts the feature of texture from an evaluation window in an image and classifies the texture class. Finally, It allocates the correspondent value for the blurring ratio. The experimental result shows that the method gives more accurate than the previous methods.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.295-309
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• 2009

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.304-304
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• 2012

III-V 화합물 태양전지는 실리콘 등 다른 태양전지에 비해 1sun상 30% 이상의 고효율을 갖고 있고 direct bandgap과 높은 이동도 등의 물질특성과 3족과 5족의 비율 조절로 같은 결정구조에서 에너지 bandgap이 다른 물질들을 만들기에 용이하여 태양전지 스펙트럼의 넓은 영역을 흡수할 수 있는 장점이 있다. 그러나 셀 자체의 물질이 실리콘에 비하여 고가여서 고성능이 요구되는 우주 인공위성 등에 적용이 되었지만, 2000년대 이후로 집광에 적용 가능한 태양전지의 연구를 거듭하여 2005년부터는 값싼 프레넬 렌즈를 이용하여 1 sun에 비해 500배 해당하는 빛을 셀에 집광하여 보다 효율을 증가시킴으로써 지상발전용에도 적용 가능한 셀을 형성하게 되었다. 더불어 태양전지의 효율을 증가시키기 위한 다양한 구조적 변화의 시도도 많이 이루어지고 있다. 최근 실리콘 태양전지의 표면에 texture 구조를 주어 높은 흡수율과 낮은 반사율을 갖게 함으로써 효율을 증가시키는 사례가 많아지고, III-V 화합물 태양전지도 texturing에 의해 증가된 효율을 발표한바 있다. 본 연구에서는 III-V 화합물 InGaP 태양전지의 window층으로 사용되는 InAlP 층에 Metal-assisted chemical etching (mac etching) 방법으로 texture 구조를 형성하여 etching 시간에 따른 InAlP층의 표면 변화와 반사율의 변화를 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.224-241
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• 2015

Multiview plus depth (MVD) videos are widely used in free-viewpoint TV systems. The best-known technique to determine depth information is based on stereo vision. In this paper, we propose a novel local stereo matching algorithm which is radiometric invariant. The key idea is to use a combined matching cost of intensity and gradient based similarity measure. In addition, we realize an adaptive cost aggregation scheme by constructing an adaptive support window for each pixel, which can solve the boundary and low texture problems. In the disparity refinement process, we propose a four-step post-processing technique to handle outliers and occlusions. Moreover, we conduct stereo reconstruction tests to verify the performance of the algorithm more intuitively. Experimental results show that the proposed method is effective and robust against local radiometric distortion. It has an average error of 5.93% on the Middlebury benchmark and is compatible to the state-of-art local methods.