• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.413-422
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• 2018

Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

• Journal of the Korean Society of Costume
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• v.57 no.9
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• pp.76-87
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• 2007

Since the emergence of postmodernism, as interest in human has increased, human face image is being highlighted as one of the themes that are addressed the most. Making the images of faces and individuals shown in costumes the subject matter, this thesis examines the internal values immanent in the images in more depth and understands them based on the Panofsky's iconological interpretation scheme. This study aims to identify designer's purposes and even their unconscious intention through iconological interpretation of faces shown in the fashion and images shown in human image, and further to present basic materials in the fashion design. This research used literature reviews and case studies, and used Panofsky's iconological interpretation theory as the scheme in order to interpret the symbolic significance implied in the images. The images of faces and individuals shown in costumes were classified into six types through historical reviews, and based on the types the images of faces and individuals shown in the fashion since the 20th century were examined. The iconological analysis of the images of faces and individuals shown in costumes based on the classification of types according to historical reviews showed parodies, cultural identity, commercial use, eroticism, respect for heros and its fiction. This study has found that all such things finally return to humanism that humans should be valued and loved the most.

• Journal of the Korean earth science society
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• v.28 no.3
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• pp.277-288
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• 2007

This study analyzed the visual images covering 'weather' and 'weather forecast' in elementary science textbooks from the Syllabus Period to the 7th national curriculum from the socio-semiotic perspective. The results showed that most of the visual images were 'realistic' which were descriptive of real world phenomena. This means that most of the visual images in elementary science text were familiar to students in every curriculum period. The power relationship in communication between images and students was very complex. The visual images in elementary science textbooks include few geometrical and alphanumeric code in every curriculum period. This study provides a new framework to interpret amount of information, functions of information, structures, and social meanings of visual images. It could be also a beginning stage to introduce the socio-semiotic perspective into choosing visual images for next science textbooks.

• Journal of the Korean Society of Costume
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• v.61 no.2
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• pp.131-151
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• 2011

The study alms to identify how the symbols and images of Greek Roman myths in contemporary fashion have been reflected in respects of meaning and forms, and to find out the organic course from meaning to forms by using Durand's classification. The results define the significance of Greek Roman myths in contemporary fashion, and systematically suggest a direction of imagination for more creative design. In the diurne regime, the symbolism of purity, heroism and fear appeared. In the nocturne regime, the symbolism of maternity and sensuality appeared. In the dramatic regime, the symbolism of androgyny appeared. The characteristics of designs contained in each symbolism are common. In this regard, it is possible to trace organic relationships in the creation of images through the verbal scheme. In addition, the verbal scheme creates archetypal images that lead to images and symbols in the socio-cultural context, so it is possible to analyze the relationships between archetypal images and the format of garments. The study examined how the archetypal images that appeared in the mythical images were expressed in garments through the verbal system.

• Imaging Science in Dentistry
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• v.40 no.2
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• pp.69-74
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• 2010

Purpose : The research was performed to predict the three dimensional relationship between the impacted mesiodens and the maxillary central incisors and the proximity with the anatomic structures by comparing their panoramic images with the CT images. Materials and Methods : Among the patients visiting Seoul National University Dental Hospital from April 2003 to July 2007, those with mesiodens were selected (154 mesiodens of 120 patients). The numbers, shapes, orientation and positional relationship of mesiodens with maxillary central incisors were investigated in the panoramic images. The proximity with the anatomical structures and complications were investigated in the CT images as well. Results : The sex ratio (M : F) was 2.28 : 1 and the mean number of mesiodens per one patient was 1.28. Conical shape was 84.4% and inverted orientation was 51.9%. There were more cases of anatomical structures encroachment, especially on the nasal floor and nasopalatine duct, when the mesiodens was not superimposed with the central incisor. There were, however, many cases of the nasopalatine duct encroachment when the mesiodens was superimpoised with the apical 1/3 of central incisor (52.6%). Delayed eruption (55.6%), crown rotation (66.7%) and crown resorption (100%) were observed when the mesiodens was superimposed with the crown of the central incisor. Conclusion : It is possible to predict three dimensional relationship between the impacted mesiodens and the maxillary central incisors in the panoramic images, but more details should be confirmed by the CT images when necessary.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.123-130
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• 2016

Purpose : The aim of this study was to compare the differences between the volumes acquired with four-dimensional computed tomography (4DCT)images with a reconstruction image-filtering algorithm and cone-beam computed tomography (CBCT) images with dynamic phantom. Materials and Methods : The 4DCT images were obtained from the computerized imaging reference systems (CIRS) phantom using a computed tomography (CT) simulator. We analyzed the volumes for maximum intensity projection (MIP), minimum intensity projection (MinIP) and average intensity projection (AVG) of the images obtained with the 4DCT scanner against those acquired from CBCT images with CT ranger tools. Results : Difference in volume for node of 1, 2 and 3 cm between CBCT and 4DCT was 0.54~2.33, 5.16~8.06, 9.03~20.11 ml in MIP, respectively, 0.00~1.48, 0.00~8.47, 1.42~24.85 ml in MinIP, respectively and 0.00~1.17, 0.00~2.19, 0.04~3.35 ml in AVG, respectively. Conclusion : After a comparative analysis of the volumes for each nodal size, it was apparent that the CBCT images were similar to the AVG images acquired using 4DCT.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.31-39
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• 2018

In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.4
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• pp.529-538
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• 1993

This study was intended to identify the hierarchy of clothing images, which is expected to be helpful in style classification and product positioning. A questionnaire consisted of 110 words expressing clothing images was developed, and eight clothing photographs were selected as stimuli. 289 female subjects aged between 22 to 37 responded to two of the eight photographs during September, 1991. 110 words were reduced to 62 words based on their independence before conducting factor analysis to identify the constructing factors of clothing images. Nine words with negative connotations were eliminated, because they are not sought in product development. To explain the hierarchy of clothing images, cluster analysis was applied. To observe the association of 53 words, dendrogram was introduced, and to interpret the result, eleven sub clusters were determined. This 11 clusters were continuously combined according to their similarities, until they integrated into one 'clothing image'. Two major division of image clusters were 'graceful and feminine image', and 'mannish and simple image'.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.