• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.1-6
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• 2020

In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.531-537
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• 2014

Through-silicon vias (TSVs) are fine, deep holes fabricated for connecting vertically stacked wafers during three-dimensional packaging of semiconductors. Measurement of the TSV geometry is very important because TSVs that are not manufactured as designed can cause many problems, and measuring the critical dimension (CD) of TSVs becomes more and more important, along with depth measurement. Applying white-light scanning interferometry to TSV measurement, especially the bottom CD measurement, is difficult due to the attenuation of light around the edge of the bottom of the hole when using a low numerical aperture. In this paper we propose and demonstrate four bottom CD measurement methods for TSVs: the cross section method, profile analysis method, tomographic image analysis method, and the two-dimensional Gaussian fitting method. To verify and demonstrate these methods, a practical TSV sample with a high aspect ratio of 11.2 is prepared and tested. The results from the proposed measurement methods using white-light scanning interferometry are compared to results from scanning electron microscope (SEM) measurements. The accuracy is highest for the cross section method, with an error of 3.5%, while a relative repeatability of 3.2% is achieved by the two-dimensional Gaussian fitting method.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.465-470
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• 2002

This paper presents a new model based method for tracking moving object from a moving camera. In the proposed method, binary model is derived from detected object regions and Hausdorff distance between the model and edge image is used as its similarity measure to overcome the target's shape changes. Also, a novel search algorithm and some optimization methods are proposed to enable realtime processing. The experimental results on our test sequences demonstrate the high efficiency and accuracy of our approach.

• The Research Journal of the Costume Culture
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• v.10 no.4
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• pp.461-472
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• 2002

The purpose of this study was to analyze the expressive style of radical feminism and post-modern feminism appearing on the contemporary denim fashion by examining pictures from professional fashion magazine Vogue. For this study, I investigated documents to study the characteristic of radical feminism and post-modern feminism and classified the contemporary denim fashion into erotic look, endrogynous look and deconstructive look. The results of study on the expression style of feminism reflected on the contemporary denim fashion were as follows: First, radical feminism emphasizes that women's sexual feature is never inferior to men's. Therefore in denim fashion, erotic style which emphasize on women's sexual beauty is represented by making hot pants, mini skirt, halter blouse of denim and by using colored jeans and flower print or beads on denim. Second, post-modern feminism has been represented by disregarding or intergrating the existed rule as refusing sexual discrimination. It has been represented in fashion as an endrogynous style by representing neutral gender image. Today, it is represented in denim fashion by mixing a different fabric with denim and matching womanish design with mannish design. Third, post-modem feminism are classified into unfixed expression of genders and the deconstructive expression of methodology. The deconstructive expression of denim fashion is represented by using the damage of fabric by making a hole or tearing intentionally or fraying edge of denim. Also the unfinished designs and transformed dressing are used to express the deconstructive character in denim.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.927-929
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• 2015

With rapid progress in digital technology, demand for multi-media imaging devices is increasing. But noise occurs due to various reasons during the process of acquiring, transmitting or processing the image data. Filters used to remove salt and pepper noise include CWMF and AWMF. In areas where the noise density is high, the removal of noise is undermined. This paper suggests an algorithm that preserves the edge while removing noise using spatial weighted.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.521-526
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• 2003

In this paper, we propose a geometric active contour model based on intensity information and curve evolution for detecting region boundaries. We put boundary extraction problem as the minimization of the difference between the average intensity of the region and the intensity of the expanding closed curves. We used level set theory to implement the curve evolution for optimal solution. It offered much more freedom in the initial curve position than a general active contour model. Our methods could detect regions whose boundaries are not necessarily defiened by gradient compared to general edge based methods and detect multiple boundaries at the same time. We could improve the result by using anisotropic diffusion filter in image preprocessing. The performance of our model was demonstrated on several data sets like CT and MRI medical images.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.16-24
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• 2011

In this paper, we developed a auto sorting system for T type welding nut using a vision inspector. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. As a result we performed numeric inspection of 0.1mm accuracy. This is impossible in old sorting system and inspector with naked eye. Also, we reduced the manufacturing unit cost to 25% and improved a production efficiency to 330%.

• Journal of IKEEE
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• v.20 no.1
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• pp.82-88
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• 2016

The detection rate of Progressive Probability Hough Transform(PPHT) is decreased when a lot of noise components exist due to an unclear or complex original image although it is quite a good algorithm that detects line segments accurately. In order to solve the problem, we propose an improved line detecting algorithm which is robust to noise components and recovers slightly damaged edges. The proposed algorithm is based on PPHT and traces a line segments by pixel and checks of it is straight. It increases the detection rate by reducing the effect of noise components and by recovering edge patterns within a limited pixel size. The proposed algorithm is applied to a lane detection method and the false positive detection rate is decreased by 30% and the line detection rate is increased by 15%.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.