• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.655-657
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• 2012

본 논문에서는 심도(Depth) 카메라로부터 실시간 획득한 RGBD 데이터에서 심도 정보 기반의 AAM(Active Appearance Models)과 나이 인식 알고리즘[1]을 통해 4 개의 AG(Age Group)으로 분류하는 실시간 얼굴 나이 인식 시스템(Real-time Facial Age Recognition System)을 설계한다. 기존의 AAM 을 이용한 실시간 얼굴 특징 추출은 평균 약 4.17%의 프레임 손실율을 보였으나, 심도 정보를 활용한 AAM 은 평균 약 0.43%의 프레임 손실율만을 보였다[5]. 본 논문에서는 심도 정보를 활용한 AAM과 병렬 처리 방법인 CUDA 를 결합하여 나이 특징을 추출하고, 실시간 시스템에 적용 가능하도록 나이 인식 알고리즘을 개선하여 실시간 나이 인식 시스템을 설계한다. 설계된 시스템은 1)머리 위치 추적, 2)얼굴 인식 및 특징점 추출, 3)나이 특징 추출, 4) 나이 특징 분석, 5) 나이 분류의 5 가지 단계를 통해 최종적으로 4 개의 AG 로 분류한다.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.69-75
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• 2014

Existing People Counting System using a single overhead mounted camera has limitation in object recognition and counting in various environments. Those limitations are attributable to overlapping, occlusion and external factors, such as over-sized belongings and dramatic light change. Thus, this paper proposes the new concept of People Counting System by Facial Age Group using two depth cameras, at overhead and frontal viewpoints, in order to improve object recognition accuracy and robust people counting to external factors. The proposed system is counting the pedestrians by five process such as overhead image processing, frontal image processing, identical object recognition, facial age group classification and in-coming/out-going counting. The proposed system developed by C++, OpenCV and Kinect SDK, and it target group of 40 people(10 people by each age group) was setup for People Counting and Facial Age Group classification performance evaluation. The experimental results indicated approximately 98% accuracy in People Counting and 74.23% accuracy in the Facial Age Group classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• Proceedings of the KACD Conference
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• 2002.05a
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• pp.350-350
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• 2002

치아 외상은 크게 fracture와 luxation injury로 분류된다. 이 중에서 영구치의 root fracture는 외상의 0.5~0.7%를 차지하는 것으로 조사되고 있다. 호발부위로는 상악 중절치가, 나이로는 11~20세에서 호발하여, 이보다 어린 나이에서는 alveolar socket의 elasticity 때문에 fracture보다는 luxation 쪽으로 많이 발생하는 것으로 보고되고 있다.(중략)

• Electronics and Telecommunications Trends
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• v.23 no.5
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• pp.89-98
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• 2008

칼코지나이드 소재는 주기율표 6족의 칼코젠 원소로 구성되는 이원계 이상의 화합물 반도체 이자 반금속으로 분류되는 소재이다. 칼코지나이드 소재는 상변화 특성 및 광변전환 특성으로 광 및 전기 메모리, 의료기기 및 광소자 등에 사용되고 있다. 최근 빠른 스위칭 특성 및 용액증착법으로 재료개발이 가능해짐에 따라 차세대 원천소재로 평가되어 전세계적으로 급격한 연구가 이루어지고 있으며 고유가로 인한 신재생에너지 등으로 활용분야가 확대되고 있다. 본 고에서는 칼코지나이드 소재의 핵심기술 및 특성, 주요기관의 최근 칼코지나이드 소재개발 동향, 연구개발 동향 및 발전전망에 대해 살펴보기로 한다.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.238-241
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• 2023

Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution. We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that our method has better performance than existing method.

• Journal of Platform Technology
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• v.11 no.5
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• pp.16-22
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• 2023

There is a growing interest in facial age estimation because many applications require age estimation techniques from facial images. In order to estimate the exact age of a face, a technique for extracting aging features from a face image and classifying the age according to the extracted features is required. Recently, the performance of various CNN-based deep learning models has been greatly improved in the image recognition field, and various CNN-based deep learning models are being used to improve performance in the field of facial age estimation. In this paper, age estimation performance was compared by learning facial features based on various CNN-based models such as AlexNet, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152. As a result of experiment, it was confirmed that the performance of the facial age estimation models using ResNet-34 was the best.

• Journal of Korea Game Society
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• v.17 no.4
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• pp.91-100
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• 2017

A player character has a significant influence on immersion. In order to understand the consumers, it is necessary to compare age and gender of player characters, which is the most basic feature of the player character. We selected a prize winning work of 15 years of USA and japanese as research subjects(in RPG and Adventure), classified age and gender of player characters and analyzed the result. In the japanese games, the ratio of pre-set player characters was 94.3% for age and 88.6% for gender. In the USA games, It was the order of the pre-set character which did not specify the age and the customized type character. In this research, we would like to contribute guidelines for player character setting to successfully advance into the USA and Japanese game market.

• Journal of Gastric Cancer
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• v.7 no.3
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• pp.124-131
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• 2007

Purpose: This study was conducted to identify prognostic factors in gastric cancer without lymph node metastasis and to specifiy which prognostic factors can be available in detail according to the depth of invasion. Materials and Methods: This retrospective study was based on the medial records of 268 gastric cancer patients who received resectional therapy from 1990 to 1999. The patients who revealed pT2NOMO, pT3NOMO, pT4NOMO on postoperative pathologic reports were enrolled. The survival rate was analyzed according to clinicopathologic and therapeutic factors. Results: According to the depth of invasion, the number of patients with pT2a, pT2b, pT3 and pT4 were 86 (32.1%), 56 (20.9%), 108 (40.3%), and 18 (6.7%) respectively. Age, depth of invasion, histological type, Borrmann type, and Lauren classification were statistically significant in the univariate analysis, and the age, the depth of invasion, and Lauren classification were independent prognostic factors identified by multivariate analysis. On multivariate analysis of subgroups according to the depth of invasion, the independent prognostic factors were age, Borrmann type, and Lauren classification in pT2, and age, Lauren classification, and vascular invasion in pT3. The prognostic factors of pT4 patients could not be analyzed due to limited sample size. Conclusion: In advanced gastric cancer patients without lymph node metastasis, age, the depth of invasion, and Lauren classification should be checked to predict prognosis. In patients with pT2 lesion among the above patients, the Borrmann type should be added in check-list.