• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.164-164
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• 2000

This paper proposes a moving window technique that extracts lanes and vehicles using the images captured by a CCD camera equipped inside an automobile in real time. For the purpose, first of all the optimal size of moving window is determined based upon speed of the vehicle, road curvature, and camera parameters. Within the moving windows that are dynamically changing, lanes and vehicles are extracted, and the vehicles within the driving lanes are classified as obstacles. Assuming highway driving, there are two sorts of image-objects within the driving lanes: one is ground mark to show the limit speed or some information for driving, and the other is the vehicle as an obstacle. Using characteristics of three-dimension objects, a neural network can be trained to distinguish the vehicle from ground mark. When it is recognized as an obstacle, the distance from the camera to the front vehicle can be calculated with the aids of database that keeps the models of automobiles on the highway. The correctness of this measurement is verified through the experiments comparing with the radar and laser sensor data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.354-354
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• 2000

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various pans so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that it is not easy to classify characters for the delicacy of the character forms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recognized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• Fashion & Textile Research Journal
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• v.6 no.6
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• pp.699-706
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• 2004

In today's fashion industry, directions for new products and high value added of fashion goods, product changes according to cycles, the shortening of life cycles, added value, planned obsolescence, and presentation is focused on fashion trends that will be selected by many consumers at the point of selling time. Therefore fashion information poses great importance and its weight is growing bigger everyday. Fashion information recognized to be important is reflected practically in the prediction of fashion changes in the fashion industry; especially, it is the first stage of the merchandising process that is the course of new product development. Presently, with some differences according to the size and specialized area of a firm, domestic fashion menufacturers obtain information from sales data of competing brands and their own, market information, consumer information based on primary data, shared fashion trend information given by domestic fashion information providing companies. Firms can not produce differentiated images and product concepts using such shared information. Although the types, importance and reflection of used information vary according to merchandising processes, all experts engaging in the merchandising of fashion products use the same shared information.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.228-233
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• 1997

An efficient corner matching algorithm is developed to minimize the amount of calculation. To reduce the amount of calculation, all available information from a corner detector is used to make model. This information has uncertainties due to discretization noise and geometric distortion, and this is represented by fuzzy rule base which can represent and handle the uncertainties. Form fuzzy inference procedure, a matched segment list is extracted, and resulted segment list is used to calculate the transformation between object of model and scene. To reduce the false hypotheses, a vote and re-vote method is developed. Also an auto tuning scheme of the fuzzy rule base is developed to find out the uncertainties of features from recognized results automatically. To show the effectiveness of the developed algorithm, experiments are conducted for images of real electronic components.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.964-968
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• 2008

Infrared thermography using high-speed infrared camera has been recognized as a powerful method for various potential applications, such as nondestructive inspection, failure analysis, stress analysis, and medical fields, due to non-contact, high-speed, and high spatial resolution at various temperature ranges. In this investigation, damage evolution due to generation of hot spots on railway brake disc was investigated using the infrared thermography method. A high-speed infrared camera was used to measure the surface temperature of brake disc as well as for in-situ monitoring of hot spot evolution. From the thermographic images, the observed hot spots and thermal damage of railway brake disc during braking operation were qualitatively analyzed. Moreover, in this investigation, the previous experimental and theoretical studies on hot spots phenomenon were reviewed, and the current experimental results were introduced and compared with theoretical prediction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• IE interfaces
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• v.8 no.1
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• pp.5-13
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• 1995

Automated vision inspection has become a vital part of computer related industries. Most of the existing inspection systems mainly utilize black and white images. In this paper, we consider an application of automated vision inspection in which cable color has to be recognized in order to detect the quality status of assembled wire harness. A back-propagation neural network is proposed to classify seven different cable colors. To represent a single point in image space, we use the ($L^*,\;a^*,\;b^*$) model which is one of commonly used color-coordinate systems in image processing. After training the neural network with ($L^*,\;a^*,\;b^*$) data obtained from color image, we tested its performance. The results show that the neural network is able to classify cable colors with high performance.

• Korean journal of applied entomology
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• v.54 no.3
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• pp.271-274
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• 2015

The genus Italochrysa Principi in Korea has been recorded with only one species, I. japonica (McLachlan) so far. Here we report that Italochrysa nigrovenosa Kuwayama is recognized for the first time in Korea. Specific description, a key based on diagnostic characters, and adult and genital images of the species are provided.

• Journal of the Korean Society of Costume
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• v.60 no.1
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• pp.28-42
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• 2010

A purpose of this study is to 6nd out how the casual and formal style clothes of stripe pattern giving variety by pattern direction, pattern width, and contrast coloration have an effect on image of wearers. For this, 192 stimuli were made and 1200 testee evaluated them using semantic differential scale. As a result, five image dimensions were drawn as a factor of attractiveness, gracefulness, activeness, visibility, and tenderness. Unlike the value contrast previously researched, it showed that chroma contrast coloration which was interacted with a color tone contrast coloration had an effect on all the 5 image dimensions. This result was recognized as significant clothes dues in evaluating the image of stripe wearers. Besides, clothing style, stripe pattern, and contrast coloration were made clear as an efficient parameter in image presentation of clothing wearers.

• Korean Journal of Plant Taxonomy
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• v.50 no.1
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• pp.1-7
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• 2020

Cynanchum L. in the subfamily Asclepiadoideae (Apocynaceae) includes four recognized species on the Korean peninsula, two of which are native to South Korea. However, the species ranges in South Korea are poorly defined. During a field survey, we discovered C. thesioides, previously unrecorded in South Korea, in Gimpo-si, Gyeonggi-do, and found an additional population of C. chinense, for which only one population has been reported in South Korea. The two taxa are considered rare species with extremely restricted distributions in South Korea, especially C. thesioides. We provide fundamental information, including descriptions, images of the habitats and morphological characters, and a taxonomic key for identification and assessments of the conservation status of Cynanchum species in Korea.