• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.133-139
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• 2012

For the examination of nuclear medicine, myocardial scan is a good method to evaluate a hemodynamic importance of coronary heart disease. but, the automatized qualitative measurement is additionally necessary to improve the decoding efficiency. we suggests the creation of cardiac three-dimensional model and model of three-dimensional cardiac thickness as a new measurement. For the experiment, cardiac reduced cross section was obtained from SPECT. Next, the pre-process was performed and image segmentation was fulfilled by level set. for the modeling of left cardiac thickness, it was realized by applying difference equation of two-dimensional laplace equation. As the result of experiment, it was successful to measure internal wall and external wall and three-dimensional modeling was realized by coordinate. and, with laplace formula, it was successful to develop the thickness of cardiac wall. through the three-dimensional model, defects were observed easily and position of lesion was grasped rapidly by the revolution of model. The model which was developed as the support index of decoding will provide decoding information to doctor additionally and reduce the rate of false diagnosis as well as play a great role for diagnosing IHD early.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.31 no.2
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• pp.130-136
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• 2005

Bone grafts are widely used in the reconstruction of osseous defects in the oral and maxillofacial region. Autogenous bone grafts are considered the gold standard in grafting of the oral and maxillofacial region, because of its osteoconductive and osteoinductive properties. Mandibular symphysis & ascending ramus bone graft have been used more frequently because of easy surgical access, reduced operative time, and following minimal morbidity. However, even though the frequent use of the anterior part of ascending ramus and the different regions of mandible, rare of the reports provide information about the quantity of bone available in this donor site. So this study was taken to evaluate & quantify the amount of bone graft material in the anterior ascending ramus regions. This study was made on 36 samples of CT image. In 3D volume image, imaginary osteotomy & segmentation were done and the dimensions and volume of the bone grafts were measured and evaluated. the average volume of the graft materials obtained from the ascending ramus was $3656.83{\pm}108.19mm^3$, and the average dimensions of graft materials were $(33.68{\pm}0.48){\times}(34.92{\pm}0.51){\times}(15.96{\pm}0.27){\times}(9.05{\pm}0.27)mm$.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.55-56
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• 2006

In prevention and diagnostic system of GIS, pattern classification is focused on the detection of unnatural patterns in PD(Partial discharge) image data. Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring complex shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for image data, fractal dimension and lacunarity have been found to be useful for recognition purposes Partial discharge(PD) occuring in GIS system is a very complex phenomenon, and more so are the shapes of the various 2-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and qunatify these pattern shapes must be explored, before recognition systems based on them could be developed. The computed fractal features(fractal dimension and lacunarity) for standard library of PD data were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made.

• Restorative Dentistry and Endodontics
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• v.46 no.4
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• pp.57.1-57.9
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• 2021

Objectives: This study aimed to compare and evaluate the porosity and pore size distribution of high-viscosity glass ionomer cements (HVGICs) and conventional glass ionomer cements (GICs) using micro-computed tomography (micro-CT). Materials and Methods: Forty cylindrical specimens (n = 10) were produced in standardized molds using HVGICs and conventional GICs (Ketac Molar Easymix, Vitro Molar, MaxxionR, and Riva Self-Cure). The specimens were prepared according to ISO 9917-1 standards, scanned in a high-energy micro-CT device, and reconstructed using specific parameters. After reconstruction, segmentation procedures, and image analysis, total porosity and pore size distribution were obtained for specimens in each group. After checking the normality of the data distribution, the Kruskal-Wallis test followed by the Student-Newman-Keuls test was used to detect differences in porosity among the experimental groups with a 5% significance level. Results: Ketac Molar Easymix showed statistically significantly lower total porosity (0.15%) than MaxxionR (0.62%), Riva (0.42%), and Vitro Molar (0.57%). The pore size in all experimental cements was within the small-size range (< 0.01 mm³), but Vitro Molar showed statistically significantly more pores/defects with a larger size (> 0.01 mm³). Conclusions: Major differences in porosity and pore size were identified among the evaluated GICs. Among these, the Ketac Molar Easymix HVGIC showed the lowest porosity and void size.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.193-200
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• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.