• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.527-535
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• 2017

In this study, we analyzed the image of artificial intelligence recognized by elementary students using semantic differential scale. First, we extracted 23 pairs of image adjectives related to perception of artificial intelligence. Adjectives were classified into three types related to recognition, emotion and ability and 827 elementary students were examined. Image factors were classified into four factors: convenience, technological progress, human-friendliness, and concern. As a result, they showed a clear image that artificial intelligence is clever, new, and complex but exciting. In comparison with variables, female students, coding experience and older students thought that artificial intelligence was more human-friendly and technological progressive.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.609-618
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• 2013

This paper proposes a fast image segmentation method that gives high segmentation performance as graph-cut based methods. Graph-cut based image segmentation methods show high segmentation performance, however, the computational complexity is high to solve a computationally-intensive eigen-system. This is because solving eigen-system depends on the size of square matrix obtained from similarities between all pairs of pixels in the input image. Therefore, the proposed method uses the small-size square matrix, which is obtained from all the similarities among regions obtained by segmenting locally an image into several regions by graph-based method. Experimental results show that the proposed multi-scale image segmentation method using the algebraic multi-grid shows higher performance than existing methods.

• The International Journal of Costume Culture
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• v.5 no.1
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• pp.58-66
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• 2002

The objectives of this study are to develope sensibility image scales for Korean traditional motifs by quantitatively measuring their images and preference and to classify them into clusters. Data were collected via a questionnaire from seven hundred twenty five Korean undergraduate students. Re experimental materials were forty eight stimuli of Korean traditional motifs with different categories, interpretation types, composition types, and application objects. The instruments consisted of 7-point polar semantic differential scales of twenty three bipolar adjectives including preference. Data were analyzed by correspondence analysis, cluster analysis, ANOVA and Duncan's multiple range test. Re major results are as follows; image scales for textile patterns and dress designs using Korean traditional motifs were constructed. The axes of sensibility image scales for both textile patterns and dress designs were defined by quality level and degree of simplicity. Second, four clusters on the scale of textile patterns and two clusters on the scale dress designs were identified. Third, in the case of textile Patterns, the preferred cluster had high-quality and classical images, while the cluster that was not preferred had a complex image. In the case of dress designs, the preferred cluster had simple and high-quality images, while the cluster that was not preferred had complex and low-quality images.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.92-102
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• 1990

A new method for determining the motion parameters (scale, rotation, translation) of 2-D image is introduced. It employs Hough transform that maps the straight lines in the input image to the points in the Hough space (HS). This method makes use of the relations between the motion of an object in input image and the translations of peak points in the HS and thus derives relating equations about motion parameters especially when scale changes are involved. The derived equations make is efficient and simple to estimate motion parameters of input image, even if the scale parameter of input image is varied. Performance of this approach on an aircraft image is provided in detail in the presence of noise.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.71-84
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• 2001

The geographical phenomena in space are well observed in the specific scale. This scale is called the operational scale. For an analysis of the optimum scale, it is needed to measure and represent the characteristics of attribute information extracted from the satellite imagery. The development of remote sensing technique makes various images with different resolution available. Researchers can select the image with optimum resolution for their analysis among various resolutions. For an effective analysis of the scale characteristics of satellite image, we investigated the characteristics of attribute information extracted from satellite image with different resolution. The two stage-procedure for exploring the optimum resolution proposed in this study was tested by applying to the satellite imagery covering Sunchon bay. This procedure can be an effective tool utilizing the scale characteristics of attribute information extracted from satellite imagery.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.6
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• pp.11-20
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• 2002

This study aims to discover the landscape image of bridges and their harmonization wish surrounding sceneries. This research utilized the basic study tool of psycho-physics and processed the case study of five types of bridges on the Han-River, Seoul. Twenty-one bridges on Han-River were classified into five categories ; the cable stayed bridge, the arch bridge, the girder bridge, the trust bridge and the suspension bridge. Also, aesthetic elements of the bridges including the form the texture, the color, the scale and the harmony were examined. The questionaires to analysis the image and harmonization with surrounding sceneries were designed using semantic differential scale and 5 point Likert scale. The results of the research were as follows. First, components representing the images of bridge landscape are classified into three types, ‘beauty’, ‘weightfulness’ and ‘friendliness’. Second, the image of each bridge as a whole turns out not to be different from each other but to be different in the context of neighboring sceneries. It was also determined that both the Cable Stayed Bridge type and the Arch Bridge type are the most attractive. But, the former does have a more masculine image, and the latter has a m[n feminine image. Third, the Cable Stayed Bridge and the Arch Bridge were evaluated highly in terms of harmonization with surrounding landscapes, while the Girder Bridge received the lowest evaluation. All of the above results suggest that the bridges should be constructed not only for beauty itself in form, color, texture and scale, but also in harmonization with the surrounding landscape. Lastly, it is desirable to do further research to find out sort specific design principles that exist between bridges and tangible surrounding landscape types.

• The Korean Journal of Rehabilitation Nursing
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• v.9 no.1
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• pp.34-41
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• 2006

Purpose: This study was to analyze the relationship the physical disability, cognitive disorder and body image. Method: The research was a descriptive relationship study. A sample is composed of 101 hospitalized stroke patients. Data were collected from November, 2005 to December, 2005. The survey instruments used in the study Sharon and Glen's physical disability scale, Lim's cognitive disorder scale and Osgood's body image scale. The collected data were analyzed frequency, percentage, mean, standard deviation, ANOVA, Duncan test, Pearsons' correlation coefficients. Result: The level of physical disability the score was 2.26, cognitive disorder 1.84 and body image 3.54. and they were relation to significant correlation. The body image showed significant negative correlation with physical disability, cognitive disorder. Physical disability showed significant positive correlation with cognitive disorder(r=.639, p=.000), and significant negative correlation with body image(r=-.420, p=.000). Cognitive disorder showed significant negative correlation with body image(r=-.620, p=.000). There were significant differences of body image by general characteristics as follows: age(p=.000), occupation(p=.004), education(p=.008), disease(p=.007). monthly income(p=.006), burden of medical expenses(p=.001), duration of stroke(p=.008). Conclusion: There was a significant correlation between physical disability, cognitive disorder and body image. there will be considered useful nursing intervention effect to physical disability, cognitive disorder and body image of stroke patients.

• The Journal of Sustainable Design and Educational Environment Research
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• v.12 no.3
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• pp.58-67
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• 2013

This study is to analyze the satisfaction and the image evaluation of landscape elements in outdoor space by types of the university campus. The results are as follows. 1) Out of outdoor elements at university campus, planting area, resting area, access road, and water feature are recognized as major landscape elements. Among them, planting area and access roads are evaluated low in terms of satisfaction levels, therefore, improvement on these elements are required. 2) In outdoor space image evaluation, university campus has image such as 'simple', 'clear', and 'safe'. By scale of universities, both 'A' university, which is the biggest in terms of size of campus, and 'B' university, which has a medium sized campus, have a positive image. However, 'C' university, which is the smallest in terms of size of campus, has a passive and negative image. 3) 6 factors are extracted through Factor Analysis for image evaluation. All of the universities show positive image in the categories of 'clarity' and 'familiarity', however, 'B' university and 'C' university show negative image in the category of 'scale'. 4) In Correlation Analysis between landscape elements satisfaction level and image evaluation, it is showed that the group of landscape facility becomes a relation factor of overall image evaluation. As a result, the higher satisfaction level goes, the better image evaluation of overall outdoor space at university campus is.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).