• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.63-71
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• 2006

Denoising and reconstruction of color images are extensively studied in the field of computer vision and image processing. Especially, denoising and reconstruction of color face images are more difficult than those of natural images because of the structural characteristics of human faces as well as the subtleties of color interactions. In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noise on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following five steps: training of canonical eigenface space using PCA, automatic extraction of facial features using active appearance model, relishing of reconstructed color image using bilateral filter, extraction of noise regions using the variance of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denoising method maintains the structural characteristics of input faces, while efficiently removing complex color noise.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.111-121
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• 2024

With strength training comes the risk of injury and the benefits of exercise. Lack of knowledge and experience or repetitions at excessive intensity can lead to injury. Adequate feedback on an exercise's progress can increase the exercise's effectiveness and reduce injuries by providing scientific data and psychological motivation. This study aimed to validate EMG equipment and examine the effects of 8 weeks of biofeedback training with wireless electromyography. A correlation analysis between the Noraxon device and Fitsig^®(EMG Prototype), a well-known instrument in the field of research, showed a moderate correlation. Statistically significant differences in humeral circumference, humeral muscle mass, and biceps and triceps strength were found between the left and right sides of the body over time, with no differences in the type of exercise. Feedback training with real-time EMG was found to be favorable for hypertrophic growth and strength improvement. Future studies should be conducted to investigate its application in sports activities further.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.483-492
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• 2019

The culture and education are very active with the active policy and support to form the government's Maker Movement. The purpose of this study is to grasp the current status of the education sector of the domestic maker movement, which is increasing recently, and to propose a plan for activating maker education for the development of a positive direction. To this end, first, the current status and problems of domestic maker training are derived through in-depth interviews with existing maker training operators and participants. Second, based on the contents of the interview script, keyword analysis and its characteristics through the qualitative survey analysis program (NVIVO) are identified. Third, based on the analysis results, we propose a plan and development direction for domestic maker education. Based on the educators who performed maker training and the students involved, professional maker teachers were required for the professionalism of education, and the expansion of maker channels and professional networking of participating students was required. In addition, there was a need for specialized programs and appropriate policy support that reflected the characteristics of maker training. This study aims at contributing to the activation of maker education, which is a major field of maker movement, by helping to improve concrete support methods, training related educators, and educational environment for maker education.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.670-683
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• 2020

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.435-438
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• 2003

A townscape has been a main factor in urban-development problems in Japan. In the townscape, keeping harmony with environment is a common goal. But useful and meaningful goals are expressing individuality and impression of the town in the townscape. In this paper, we propose the colony planning support system system to improve the townscape. The system finds propositional colour combinations based on three elements, town image, colour harmony, and cost. The targets of this model are mostly townscapes in residential areas that already exist, In this paper, we introduce the construction of a Kansei evaluation model to quantify the impression. First, we conducted computer-based evaluational experiments for 20 subjects using the SD method to clarify the relationship between town image and street colours. We chose 16 adjective words related to town image and prepared 100 colour picture samples for the evaluation. After the experiments, we constructed the model using a neural network for each word. We chose 62 experimental results for the training data of the neural network and 20 results for the testing data. Each colour in the data was selected to have unique hue, brightness or saturation attributes, After the construction, we tested the model for accuracy. We input the testing data into the constructed model and calculated errors between the output from the model and the experimental results. Testing of the model showed that the model worked well for more than 80％ of the samples. The model demonstrated influences of colours on the town image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• The Journal of Korean Association of Computer Education
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• v.19 no.2
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• pp.11-20
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• 2016

We enter the age of "a software revolution. The core competence for people living in these times as based on software competence to tackle the problem in computational thinking. Solve the problem of the basis of computational thinking to cultivate these competences to adopt a programming. As a result, this text to study is raising the computational thinking competencies for software training in recognition of the need to learn. A couple of times to develop content for the purposes of computational thinking competencies, Two based on the opinions of experts across extract the contents of a computational thinking. Further, these studies based on accident, we look forward to develop in the process of computational thinking competences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.595-600
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• 2019

Educational tools for infants and younger students are becoming smarter as ICT-based digital technology convergence extends according to the development of technology. As the digital interaction function of smart education tools gives students greater immersion and fun, a learning might become a play to the students. The technologies used in the implementation of smart education tools come from the disciplines of robotics, computer engineering, programming, and engineering and mathematical foundations and these can be integrated into the field of education itself. This paper designs and implements a product based on optimized physical computing for R&D and education in consideration of the characteristics of educational tool robots used in the field education. It was developed to enable physical education for sensing information processing, software design and programming practice training that is the basis of robot system.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.