• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.39-46
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• 2009

Many lip region detection methods have been developed in PC environment. But the existing methods are difficult to run on real-time in resource limited mobile devices. To solve the problem, this paper proposes a real-time lip region detection method for lipreading in Mobile device. It detects face region by using adaptive face color information. After that, it detects lip region by using geometrical relation between eyes and lips. The proposed method is implemented in a smart phone with Intel PXA 270 embedded processor and 386MB memory. Experimental results show that the proposed method runs at the speed 9.5 frame/see and the correct detection rate was 98.8% for 574 images.

• ETRI Journal
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• v.44 no.1
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• pp.51-61
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• 2022

In this paper, we introduced a novel voxel-wise UV parameterization and view-dependent texture synthesis for the immersive rendering of a truncated signed distance field (TSDF) scene model. The proposed UV parameterization delegates a precomputed UV map to each voxel using the UV map lookup table and consequently, enabling efficient and high-quality texture mapping without a complex process. By leveraging the convenient UV parameterization, our view-dependent texture synthesis method extracts a set of local texture maps for each voxel from the multiview color images and separates them into a single view-independent diffuse map and a set of weight coefficients for an orthogonal specular map basis. Furthermore, the view-dependent specular maps for an arbitrary view are estimated by combining the specular weights of each source view using the location of the arbitrary and source viewpoints to generate the view-dependent textures for arbitrary views. The experimental results demonstrate that the proposed method effectively synthesizes texture for an arbitrary view, thereby enabling the visualization of view-dependent effects, such as specularity and mirror reflection.

• ETRI Journal
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• v.44 no.1
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• pp.73-84
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• 2022

The moving picture experts group (MPEG) immersive video (MIV) technology has been actively developed and standardized to efficiently deliver immersive video to viewers in order for them to experience immersion and realism in various realistic and virtual environments. Such services are provided by MIV technology, which uses multiview videos as input. The pruning process, which is an important component of MIV technology, reduces interview redundancy in multiviews videos. The primary aim of the pruning process is to reduce the amount of data that available video codec must handle. In this study, two approaches are presented to improve the existing pruning algorithm. The first method determines the order in which images are pruned. The amount of overlapping region between the source views is then used to determine the pruning order. The second method considers global region-wise color similarity to minimize matching ambiguity when determining the pruning area. The proposed methods are evaluated under common test condition of MIV, and the results show that incorporating the proposed methods can improve both objective and subjective quality.

• Journal of Digestive Cancer Research
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• v.5 no.1
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• pp.50-54
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• 2017

Pancreatic neuroendocrine tumors are rare pancreatic neoplasms comprising 1-2% of all pancreatic tumors and typically present high attenuating mass on arterial and venous phase images, due to their rich capillary network. A 70-year-old South Korean female visited our hospital presenting with jaundice and dark urine color. She had received an operation for treatment of small bowel perforation seven years ago. On physical examination, icteric sclera was observed but otherwise unremarkable. Laboratory tests were abnormal liver function test and suspected obstructive jaundice. Computed tomography revealed 4 cm sized cystic mass lesion with homogeneous low attenuation in the head of pancreas and distal common bile duct was compressed by the mass. During review of past medical records, we found that the mass was observed and measured about 1.7 cm seven years ago. To resolve obstructive jaundice, pylorus preserving pancreaticoduodenectomy was performed and diagnosed with well differentiated pancreatic neuroendocrine carcinoma with intermediate grade.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.589-603
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• 2023

Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.6
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• pp.1137-1156
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• 2023

With the advent of artificial intelligence (AI) during the Fourth Industrial Revolution, the fashion industry has simplified the production process and overcome the technical difficulties of design. This study anticipates likely changes in the digital age and develops a model that will allow consumers to design textile patterns using AI technology. Previous studies and industrial examples of AI technology's use in the textile design industry were investigated, and a textile pattern was developed using an AI algorithm. A new textile design model was then proposed based on its application to both virtual and physical clothing. Inspired by traditional Korean masks and props, AI technology was used to input color data from open application programming interface images. By inserting these into various repeating structures, a textile design was developed and simulated as garments for both virtual and real garments. We expect that this study will establish a new textile design development method for Generation Z, who favor customized designs. This study can inform the use of personalization in generative textile design as well as the systemization of technology-driven methods for customized and participatory textile design.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.89-94
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• 2024

Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification, and so forth is composed of text contents written in different languages/scripts. These scripts may be in the form of different indic numerals in a single document page. Due to this reason, building a generic recognizer that is capable of recognizing handwritten indic digits written by diverse writers is needed. Also, a lot of work has been done for various non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses with only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is proposed to recognize all available indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of Capsule Network with hand crafted Bag of Feature (BoF) extraction method. Along the way, we analyze (1) the successes (2) explore whether this method will perform well on more difficult conditions i.e. noise, color, affine transformations, intra-class variation, natural scenes. Experimental results show that the hybrid method gives better accuracy in comparison with Capsule Network.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.130-135
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• 2024

The Stable diffsuion AI tool is popular among designers because of its flexible and powerful image generation capabilities. However, due to the diversity of its AI models, it needs to spend a lot of time testing different AI models in the face of different design plans, so choosing a suitable general AI model has become a big problem at present. In this paper, by comparing the AI images generated by two different Stable diffsuion models, the advantages and disadvantages of each model are analyzed from the aspects of the matching degree of the AI image and the prompt, the color composition and light composition of the image, and the general AI model that the generated AI image has an aesthetic sense is analyzed, and the designer does not need to take cumbersome steps. A satisfactory AI image can be obtained. The results show that Playground V2.5 model can be used as a general AI model, which has both aesthetic and design sense in various style design requirements. As a result, content designers can focus more on creative content development, and expect more groundbreaking technologies to merge generative AI with content design.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Journal of Fashion Business
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• v.28 no.3
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• pp.104-121
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• 2024

This study examined the integration of futurist fashion in 4th-generation K-pop girl groups, focusing on their world views, music videos, and fashion images. The key aim was to identify and analyze distinctive elements of futurist fashion within K-pop. K-pop's global popularity is driven by dynamic music, choreography, and avant-garde fashion. Futurism, an art movement emphasizing technology and innovation, continues to influence contemporary fashion trends in K-pop. This study seeks to provide insights into symbolic meanings and expressions of futurist fashion in 4th generation K-pop girl groups. Groups such as Gidle, Aespa, IVE, LE SSERAFIM, and New Jeans were analyzed. Data were collected from their music videos, lyrics, and costumes, focusing on silhouette, color, material, and pattern. This study highlights the significant role of futurist fashion in K-pop, showing how 4th-generation girl groups lead in integrating these elements. This research provides valuable insights for understanding and further exploring the evolution of K-pop fashion.