• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.277-283
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• 2021

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.

• The Research Journal of the Costume Culture
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• v.19 no.6
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• pp.1259-1271
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• 2011

This study investigates and compares the changes of naturalism in fashion presented over a 30-year period to understand the diversity of naturalism in fashion. Data were collected from 59 volumes of the "Vogue" magazine for January and July in each year from 1980 to 2009. The data used for content analysis consists of 440 words and these were condensed into three periods according to decade(1980~1989, 1990~1999, and 2000~2009). The selected words were classified into four sub-themes according to the previous research definitions such as primitive look, natural look, eco look and handcrafted look. The results are as follows. First, naturalism of fashion was highly presented in the 1980s but the percentages of naturalism in three decades were all more or less similar. Especially, natural look appeared more in the 1980s and eco look was in the 1990s, while natural look, eco look and handcrafted look were found all together in the 2000s. Second, naturalism of fashion showed higher frequency of F/W seasons in the 1990s, while S/S seasons in the 1990s and 2000s. In particular, natural look was presented more at S/S seasons. The sub-themes coexistence were presented in the 24 seasons out of 59 seasons and showed more variously in the 2000s. Third, the words selected from sub-themes of naturalism in fashion demonstrated the differences by decade.

• Journal of the Korean Society of Costume
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• v.50 no.7
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• pp.75-96
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• 2000

Modern clothing is approached in the level of art, and it becomes the means of expressing individuality in modern society uniformed by the development of information society. In this modern society, modern men need the recovery of humanity and expectations of handcrafted skills for the succession of tradition culture. Prior to expressing individuality, we should examine our traditional culture and combine it with western culture. Quit started to be used for practical purpose such as life items, but it is expanded to the fields of art. Used in dress and its ornaments design, quilt is often applied to the addition of aesthetic factors or cubic material feelings by transforming its warming effect. Hereby, this study has a purpose to create high value added modern dross and its ornaments design by expressing the modern clothing is approached in the level of art, and it becomes the means of expressing individuality roe-dimensional characteristics of quilt, and applying the color combination and the surface of butterfly wings to dress and its ornaments design. The following are the results of this study. 1. The concept of quilt started for practical purposes, but it is expanded to decoration in modern times, and it is confirmed that quilt can be variously applied to handcrafted modern design. 2. Quilt removes the plane character of textile and it riches the three-dimensional material of dress and its ornaments. Applying these characteristics, the expression of transparent wings were possible with 3 transparent layers of textile. 3. The spledid color of butterfly wings are expressed by coloring oganza and felt, and the various colors of felt showed rich color gradation. 4. The form and pattern of butterfly wings are applied as modeling form and line, and the transformation. repetition and expansion of unit forms determined the form of quilting lines. By designing the characteristics of back wings for the composition line of clothing, the form characteristics of a motif could be emphasized. 5. By using felt, oganza, Damdam yarn and ostritch feathers in expressing butterfly wings, the warm material of Linbun is felt, and the tip hair of wings are expressed by croche techniques using Damdam yarn.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.91-102
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• 2019

In autonomous driving systems, the ability to classify pedestrians in images captured by cameras is very important for pedestrian safety. In the past, after extracting features of pedestrians with HOG(Histogram of Oriented Gradients) or SIFT(Scale-Invariant Feature Transform), people classified them using SVM(Support Vector Machine). However, extracting pedestrian characteristics in such a handcrafted manner has many limitations. Therefore, this paper proposes a method to classify pedestrians reliably and effectively using CNN's(Convolutional Neural Network) deep features and transfer learning. We have experimented with both the fixed feature extractor and the fine-tuning methods, which are two representative transfer learning techniques. Particularly, in the fine-tuning method, we have added a new scheme, called M-Fine(Modified Fine-tuning), which divideslayers into transferred parts and non-transferred parts in three different sizes, and adjusts weights only for layers belonging to non-transferred parts. Experiments on INRIA Person data set with five CNN models(VGGNet, DenseNet, Inception V3, Xception, and MobileNet) showed that CNN's deep features perform better than handcrafted features such as HOG and SIFT, and that the accuracy of Xception (threshold = 0.5) isthe highest at 99.61%. MobileNet, which achieved similar performance to Xception and learned 80% fewer parameters, was the best in terms of efficiency. Among the three transfer learning schemes tested above, the performance of the fine-tuning method was the best. The performance of the M-Fine method was comparable to or slightly lower than that of the fine-tuningmethod, but higher than that of the fixed feature extractor method.

• The Korean Journal of Community Living Science
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• v.22 no.1
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• pp.103-113
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• 2011

The purpose of the study was to examine formative features of embroidered norigae in form, color, pattern and expressive technique through positive analysis of relics and various of collections of work and to consider expressive features of embroidered norigae. The results of the study were as follows. First, embroidered norigae has handcrafted decoration. it was made by being sewed for women longing for their family's happiness. Embroidered norigae is a dress worn by women that is hung on a coat string or the waist part of a skirt. Second, it is eco-environmental. Embroidered norigae applied things seen in nature such as flowers, butterflies and bees to its pattern. Third, it has practicality. Embroidered norigae has high practical value besides a decorative function. Needle case norigae and incense case norigae provide functions in accordance with women's wisdom and skill as well as practicality. Fourth, it is praying for good luck. Women embroidered patterns symbolizing their desires in life such as their family's happiness, wealth, many sons and a long life. Fifth, it has balance and harmony. The knot of embroidered norigae has a perfect symmetry in the front/back part and in the right/left part. And the main body and tassel are symetrical in the right/left part, which gives stability and comfortableness. Embroidered norigae is classified into knot, main body and decorative part in its form. The three kinds express their unique beauty by being harmonized together. Finally, it has a property of melody. Movement of the tassel has a property of melody shaken by the wind and movement of its wearer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of the Korea Fashion and Costume Design Association
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• v.8 no.2
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• pp.47-59
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• 2006

The purpose of this study is to grasp the meaning of retro-fashion, to research the formative feature characteristic and aesthetic values. Retro-fashion is one of the expression of spatiotemporal-eclecticism, The formative of modern Retro-fashion are as follows: First, Retro-fashion based on spatiotemporal-eclecticism have been come from 40s, 50s, 60s, 70s, 80s style of time, and the asia, africa, middle east, latin America in region. Second, the design inspiration and technique is used more primitive crafts and decoration like handcrafted material, handmade ornaments as dyeing and embroidery of bohemian, jacqwuard pattern, oriental beads, applique, new hippie touch, patchwork, smocking, primitive button, woods, ethnic motives. Third, modern Retro-fashion is reflection of human feelings as nostalgia from the past, it supplies the sense of fashion creativity and new ideas.