• Korean Journal of Human Ecology
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• v.10 no.4
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• pp.399-409
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• 2001

The purpose of this study was to evaluate the preference of image for modern application of Korean traditional patterns. A survey was conducted using the random selection among female undergraduate students in Daegu city. The degree of interest and preference in Korean traditional style or something like that measured by 5 scale method. And then they were classified into two groups which were interest/non-interest group, and preference/non-preference group. The image of Korean traditional patterns consisted of semantic differential scales. Frequency, percentage and mean were analyzed, for difference of groups t-test was analyzed. The results were as fellows; 1. For the degree of interest for Korean traditional patterns, it was showed that 53.8% of total respondents took interest and about 40.4% of them had preference for traditional patterns. the correlation coefficient of the degree of interest and preference was 0.782(p<0.01) and showed that the positive correlation was high. 2. Among 20 kinds of Korean traditional patterns, the degree of preference for the patterns of plants and nature was quite high whereas that for the patterns of geometrical things was low relatively. 3. It was evaluated that pattern of nature was fresh, refined and womanly image generally. It was evaluated that pattern of plants was womanly, fresh, weak, light and soft image and that of animals was heavy, splendid, high-class, manly, strong and positive image. It was evaluated that pattern of geometrical things was the most refined image and high-class, rigid and strong. 4. The statistical significance of mean between interest/non-interest group was showed statistically in the patterns of clouds, mountains, lotus, apricot, orchid, dragon, phoenix and bogey. In case of pattern of orchids, the degree of preference was most different between interest/non-interest group. 5. The pattern of plants showed the most different evaluation for images between interest/non-interest group. For refined/old-fashioned polar adjective images, the interest group evaluated the pattern of plants more refined. 6. For pattern of orchids, the difference of degree of preference between preference/non-preference group was most remarkable in Korean traditional patterns. 7. The pattern of geometrical things showed the most different evaluation for images between preference/non-preference group. For warm/cool polar adjective images, the preference group evaluated the pattern of geometrical things cooler.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.73-82
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• 2005

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.5
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• pp.398-403
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• 2011

Recently, Near-infrared (NIR)colorant is intriguing and attractive but full of challenges. Although some cyanine colorant have been commercialized, near-infrared colorant with intensive NIR absorption, good chemical and photo-stability, and high solubility still remain as target compound. Certain polycyclic aromatic compounds such as quaterrylene represent a key class of NIR colorant and also give rise to outstanding physical and chemical properties after appropriate chemical modification. In this study, We have tried to introduceimide functional group to quaterrylene in order to give chemical and thermal stability. Finally, N,N'-bis (2,6-diisopropylphenyl)-quarterrylene-3,4:13,14-tetracarboximide was synthesized and evaluated its properties using $^1H$ NMR, Maldi-tof, TGA, and UV/VIS/NIR spectroscopy as NIR colorant. The quaterrylene bisimide compound exhibit a excellent thermal stability and chemical stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.

• The Journal of Natural Sciences
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• v.5 no.2
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• pp.33-38
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• 1992

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented for multime-dia teleconference. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. We compare the coding efficiency of this technique with that of a well established technique (discrete cosine transform (DCT) image coding).

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1587-1592
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• 2023

Time-encoded imagers (TEI) are important class of instruments to search for potential radioactive sources to prevent illicit transportation and trafficking of nuclear materials and other radioactive sources. The energy of the radiation cannot be known in advance due to the type and shielding of source is unknown in practice. However, the response function of the time-encoded imagers is related to the energy of neutrons or gamma-rays. An improved image reconstruction method based on MLEM was proposed to correct for the energy induced response difference. In this method, the count vector versus time was first smoothed. Then, the preset response function was adaptively corrected according to the measured counts. Finally, the smoothed count vector and corrected response were used in MLEM to reconstruct the source distribution. A one-dimensional dual-particle time-encode imager was developed and used to verify the improved method through imaging an Am-Be neutron source. The improvement of this method was demonstrated by the image reconstruction results. For gamma-ray and neutron images, the angular resolution improved by 17.2% and 7.0%; the contrast-to-noise ratio improved by 58.7% and 14.9%; the signal-to-noise ratio improved by 36.3% and 11.7%, respectively.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1176-1194
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• 2018

The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.131-139
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• 2023

In this study, we are purposed to find the educational effect of practical training using a 3D printed structure-based blind box on multidimensional radiographic image interpretation. The subjects were 83 (male: 49, female: 34) 2nd year radiological science students who participated in the digital medical imaging practice that was conducted for 3 years from 2020 to 2022. The learning method used 3D printing technology to print out the inside structure of the blind box designed by itself. After taking X-rays 3 times (x, y, z axis), the structure images in the blind box were analyzed for each small group. We made the 3D structure that was self-made with clay based on our 2D radiographic images. After taking X-rays of the 3D structure, it was compared whether it matches the structural image of the blind box. The educational effect for the practical training surveyed class faithfulness, radiographic image interpretation ability (attenuation concept, contrast concept, windowing concept, 3-dimensional reading ability), class satisfaction (interest, external recommendation, immersion) on a 5-point Likert scale as an anonymous student self-writing method. As a result, all evaluation items had high positive effects without significant differences between males and females. Practical education using blind boxes is a meaningful example of radiology education technology using 3D printing technology, and it is expected to be used as content to improve students' problem-solving skills and increase satisfaction with major subjects.