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통계적 얼굴 모델을 이용한 부분적으로 가려진 얼굴 검출
서정인(Jeongin Seo),박혜영(Hyeyoung Park) Korean Institute of Information Scientists and Eng 2014 정보과학회논문지 Vol.41 No.11
Face detection refers to the process extracting facial regions in an input image, which can improve speed and accuracy of recognition or authorization system, and has diverse applicability. Since conventional works have tried to detect faces based on the whole shape of faces, its detection performance can be degraded by occlusion made with accessories or parts of body. In this paper we propose a method combining local feature descriptors and probability modeling in order to detect partially occluded face effectively. In training stage, we represent an image as a set of local feature descriptors and estimate a statistical model for normal faces. When the test image is given, we find a region that is most similar to face using our face model constructed in training stage. According to experimental results with benchmark data set, we confirmed the effect of proposed method on detecting partially occluded face.
타이어 구름 저항 측정 방법 간 최적화된 추정 방법 연구 및 적용
서정인(Jeongin Seo),서명규(Myoungkyu Seo),염기호(Kiho Yum),장영석(Youngseok Jang),이기연(Ghiyoun Lee) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
To measure tire rolling resistance, majority of the industry uses two methods, ISO 28580 and SAE J1269. Differences of test procedures between them result in tradeoffs in measurement time and coverage. ISO 28580 requires 2 hours per tire measurement and it only can represent resistance under limited driving condition. on the other hands SAE J1269 requires 4 hours per tire but can represent resistances under various axial loads and pressures. Thus there has been a requirement to estimate various resistances by using simpler resistances. This study describes an optimized method to estimate tire resistance measured on SAE J1269 by using tire resistance measured on ISO 28580. This paper suggests building linear regression models between ISO 28580 and SAE J1269 on several areas separated by axial loads and tire pressures. The suggested method showed R2 of 0.9 and mean absolute error of 0.1.
합성곱 신경망과 영상 개선 신경망을 이용한 저해상도 영상 객체 인식
최인재(Injae Choi),서정인(Jeongin Seo),박혜영(Hyeyoung Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.8
Recently, the development of deep learning technologies such as convolutional neural networks have greatly improved the performance of object recognition in images. However, object recognition still has many challenges due to large variations in images and the diversity of object categories to be recognized. In particular, studies on object recognition in low-resolution images are still in the primary stage and have not shown satisfactory performance. In this paper, we propose an image enhancement neural network to improve object recognition performance of low resolution images. We also use the enhanced images for training an object recognition model based on convolutional neural networks to obtain robust recognition performance with resolution changes. To verify the efficiency of the proposed method, we conducted computational experiments on object recognition in a low-resolution environment using the CIFAR-10 and CIFAR-100 databases. We confirmed that the proposed method can greatly improve the recognition performance in low-resolution images while keeping stable performance in the original resolution images.