• Research in Mathematical Education
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• v.5 no.1
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• pp.1-12
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• 2001

Generating fractal images by graphing calculators such as TI_92 combines several important features, which convey the excitement of a living, changing mathematics appropriate to secondary or post-secondary students. The topic of fractal geometry can be illustrated using natural objects such as snowflakes, leaves and ferns. These complex and natural forms are often striking fantastic and beautiful. The examples highlight the fact that complex, natural behaviors can result from simple mathematical rules such as those embodied in iterated function systems(IFS). The visual splendor beauty of fractals, in concert with their ubiquity in nature, revels the intellectual beauty of nonlinear mathematics in a compelling way. The window is now open for students to experience and explore some of the wonder of fractal geometry.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.321-329
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• 1999

Moving Object segmentation extracts an interested moving object on a consecutive image frames, and has been used for factory automation, autonomous navigation, video surveillance, and VOP(Video Object Plane) detection in a MPEG-4 method. This paper proposes new segmentation method using difference images are calculated with three consecutive input image frames, and used to calculate both coarse object area(AI) and it's movement area(OI). An AI is extracted by removing background using background area projection(BAP). Missing parts in the AI is recovered with help of the OI. Boundary information of the OI confines missing parts of the object and gives inital curves for active contour optimization. The optimized contours in addition to the AI make the boundaries of the moving object. Experimental results of a fast moving object on a complex background scene are included.

• Korean Institute of Interior Design Journal
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• v.16 no.6
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• pp.172-180
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• 2007

As library have become changed. Beyond conventional simple functions such as data storing and recording, the functions of modern public libraries are expended to various directions, for example functions of encouraging residents to utilize information and participate in cultural activities and also a function of life-long education, resulting in playing a role as complex space. As the role of public libraries changes into a complex one, spatial planning reflecting this change is required. Spatial arrangements, furniture arrangements and interior images in 12 public libraries in Seoul were analyzed. As a result of the study, it was found that in spatial arrangements, many public libraries linked spaces by functions. Furniture arrangements have been changing from the closed arrangement to the open arrangement. Interior images were identified as static and simple except digital information space. Accordingly, public libraries have became needed to play a proper role of community facilities for local residents by planning furniture arrangements and conducting interior designs taking characteristics of many different users into consideration.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.844-851
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• 2019

In this paper, we propose a loss function in CNN that introduces inter-class amplitudes to increase inter-class loss and reduce intra-class loss to increase of face recognition performance. This loss function increases the distance between the classes and decreases the distance in the class, thereby improving the performance of the face recognition finally. It is confirmed that the accuracy of face recognition for visible light image of proposed loss function is 99.62%, which is better than other loss functions. We also applied it to face recognition of visible and near-infrared complex images to obtain satisfactory results of 99.76%.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.63-71
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• 2006

Denoising and reconstruction of color images are extensively studied in the field of computer vision and image processing. Especially, denoising and reconstruction of color face images are more difficult than those of natural images because of the structural characteristics of human faces as well as the subtleties of color interactions. In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noise on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following five steps: training of canonical eigenface space using PCA, automatic extraction of facial features using active appearance model, relishing of reconstructed color image using bilateral filter, extraction of noise regions using the variance of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denoising method maintains the structural characteristics of input faces, while efficiently removing complex color noise.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.173-175
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• 2017

With the rapid progress of the digital area has come the increase in demand for multi-media services. Imaging processing as a result is being hailed as a technological field that can offer smart and efficient methods for the processing and analysis of images. In general, noise exist in various types, depending on the cause and form. Some leading examples of noise are AWGN(additive white Gaussian noise), salt and pepper noise and complex noise. This study suggests an algorithm to remove complex noise by using the standard deviation and noise density of the partial mask in order to effectively remove complex noise in images.

• Journal of Engineering Education Research
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• v.20 no.6
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• pp.68-75
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• 2017

This study has a purpose to analyse changes in perception and image about engineering and technology of students who participated in the engineering camp. To achieve this objective, the questions were as follows. 1) What about participants' image difference for engineering before and after participating the engineering camp 2) What about participants' image difference for technology before and after participating the engineering camp. For this study, the program was progressed from Aug in 2017 and the data was collected from 88 students, middle school seniors and high school freshmen. The results of this study were as follows: First, secondary students perceived 'valuable(6.74)', 'meaningful(6.73)', 'rich(6.40)', 'collaborative(6.42)', 'nice(6.22)' as high image rank of the positive response for engineering. On the other hand, 'complex(3.59)', 'labored(3.80)', 'hard(4.66)', 'dangerous(4.48)', 'cold(4.86)' were perceived as low image rank of the negative response for engineering. We can realize that they generally has the image that engineering is valuable, meaningful and nice but also labored, complex and hard. The students who participated in the engineering camp showed the greatest difference in 'complex - simple' and 'dangerous - safe' engineering categories before and after the camp, followed by 'cold - hot', 'labored - easy', and 'hard - soft', respectively. Second, secondary students perceived 'meaningful(6.58)', 'valuable(6.55)', 'wide(6.38)', 'nice(6.37)', 'strong(6.25)' as high image rank of the positive response for technology. On the other hand, 'complex(3.85)', 'labored(3.93)', 'hard(4.62)', 'dangerous(4.72)', 'cold(5.05)' were perceived as low image rank of the negative response for technology. The students who participated in the engineering camp had the big change in 'hard - soft' technology category before and after the camp, followed by 'complex - simple', 'labored - easy', 'theoretical - practical' and 'dangerous - safe', respectively. We can see that the negative images for technology which were complex, labored, dangerous, theoretical was improved with positive image such as simple, easy, safe and practical, after conducting the engineering camp. In conclusion, both image recognitions for engineering and technology have improved after the camp. It means that interesting and entertaining engineering-technology program can boost interests in engineering and technology which felt difficult, so that images about them can be turned out positive. Also, it is possible to reduce avoidance of natural science and engineering subjects, as part of the purpose of training creative talents in science and engineering, so it can be said that the engineering camp is highly meaningful because it can lead students into the field of science and engineering.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.797-807
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• 2008

This paper proposes a new algorithm of extracting forward vehicle areas using the acquired lanes and road area information on road images with complex background to improve the efficiency of the vehicle detection. In the first stage, lanes are detected by taking into account the connectivity among the edges which are determined from a method of chain code. Once the lanes proceeding to the same direction with the running vehicle are detected, neighborhood roadways are found from the width and vanishing point of the acquired roadway of the running vehicle. And finally, vehicle areas, where forward vehicles are located on the road area including the center and neighborhood roadways, are extracted. Therefore, the proposed scheme of extracting forward vehicle area improves the rate of vehicle detection on the road images with complex background, and is highly efficient because of detecting vehicles within the confines of the acquired vehicle area. The superiority of the proposed algorithm is verified from experiments of the vehicle detection on road images with complex background.