• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.78-87
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• 2006

In this paper. we propose an effective mask estimation scheme for missing-feature reconstruction in order to achieve robust speech recognition under unknown noise environments. In the previous work. colored noise is used for training the mask classifer, which is generated from the entire frequency Partitioned signals. However it gives a limited performance under the restricted number of training database. To reflect the spectral events of more various background noise and improve the performance simultaneously. a new Bayesian classifier for mask estimation is proposed, which works independent of other frequency bands. In the proposed method, we employ the colored noise which is obtained by combining colored noises generated from each frequency band in order to reflect more various noise environments and mitigate the 'sparse' database problem. Combined with the cluster-based missing-feature reconstruction. the performance of the proposed method is evaluated on a task of noisy speech recognition. The results show that the proposed method has improved performance compared to the Previous method under white noise. car noise and background music conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.1
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• pp.9-18
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• 1987

An electro-optic system using linear photosensitive Charge Coupled Devices(CCDs) having dummy pixels has been proposed for realzation of linear prodictor in the differential pulse code modulation(DPCM). The system consists of three components as conventional system:input light source, spatial filter(mask) and CCD line scanning sensor. For the delay time due to the dummy pixels in CCD, modifying conventional mask, a new dispersive mask is proposed, of which every prediction coefficient is dispersed on the more than one element, the characteristics of the system using the proposed dispersive mask are analyzed theoretically and verified with experiment.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1997.05a
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• pp.51-54
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• 1997

방독마스크 수명예측을 위하여는 여러 모델식이 제안되었으며, Cohen등은 bed-residence 흡착 모델을 사용하여 정화통에서 채취한 활성탄을 carbon tube에 bed-residence time이 같게 충전시켜 습도에 따른 정화통의 수명을 예측하였다. 그리고, Mover는 Potential Jonas 모델을 적용하여 환경적 조건들과 아세톤에 대하여 유기증기 정화통의 특성을 묘사하였다. (중략)

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.97-98
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• 2022

코로나바이러스-19 팬데믹 이후 매일 수만 명의 환자가 발생하고 있다. 보건당국은 사람들의 생활 안전을 보호하기 위해 공항, 정류장 등 공공장소에서는 반드시 마스크를 착용하라고 지시하고 있다. 마스크를 착용하는 목적은 감염으로부터 신체를 보호하고 바이러스 전파와 확산을 막기 위한 것이다. 공공장소에서는 많은 인원에 대한 일괄적인 마스크 착용 검사를 하기 어렵고, 육안으로 확인하는 마스크 착용 검사 방법은 인파가 몰리는 장소에서 검사 효율이 떨어지며 누락되는 경우도 많이 발생한다. 본 연구에서는 입력 이미지에 존재하는 얼굴 영역을 YOLOv4와 YOLOv5 모델을 통해 예측하여 마스크의 착용 여부를 판단하되, 앙상블 기법을 적용하여 보다 효과적인 BB(Bounding Box) 추출 및 마스크 착용 탐지 기법을 적용한다. 따라서 공공장소의 마스크 착용실태를 효과적으로 모니터링 할 수 있는 방법을 제안한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.395-400
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• 2020

Coronavirus disease 2019 (COVID-19) was identified in December 2019 in China and has spread globally, resulting in an ongoing pandemic. Because COVID-19 is spread mainly from person to person, every person is required to wear a facemask in public. On the other hand, many people are still not wearing facemasks despite official advice. This paper proposes a method to predict whether a human subject is wearing a facemask or not. In the proposed method, two eye regions are detected, and the mask region (i.e., face regions below two eyes) is predicted and extracted based on the two eye locations. For more accurate extraction of the mask region, the facial region was aligned by rotating it such that the line connecting the two eye centers was horizontal. The mask region extracted from the aligned face was fed into a convolutional neural network (CNN), producing the classification result (with or without a mask). The experimental result on 186 test images showed that the proposed method achieves a very high accuracy of 98.4%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.338-347
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• 2005

In this paper, a decimation-in-time search direction algorithm for displacement prediction of moving object is proposed. The initialization of the proposed algorithm for moving direction prediction is performed by detecting moving objects at sequential frames and by obtaining a moving angle and a moving distance. A moving direction of the moving object at current frame is obtained by applying the decimation-in-time search direction mask. The decimation-in-tine search direction mask is that the moving object is detected by thinning out frames among the sequential frames, and the moving direction of the moving object is predicted by the search mask which is decided by obtaining the moving angle of the moving object in the 8 directions. to examine the propriety of the proposed algorithm, velocities of a driving car are measured and tracked, and to evaluate the efficiency, the proposed algorithm is compared to the full search algorithm. The evaluated results show that the number of displacement search times is reduced up to 91.8$\%$ on the average in the proposed algorithm, and the processing time of the tracking is 32.1ms on the average.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.211-214
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• 2009

H.264/AVC의 인트라 예측에서 미리 현재 블록 내의 정보 및 이전 블록의 예측 모드 정보 등을 이용하여 현재 블록의 예측 부호화 블록 크기가 결정되었을 경우, 예측된 블록 크기에 적합한 예측 모드 결정이 요구된다. 이에 사전에 결정된 예측 블록 크기 정보와 주변 블록과의 화소 변화량을 계산하여 예측 모드를 결정하는 기법을 제안하고 성능을 평가한다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.113-119
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• 2023

Despite the lifting of the mandatory wearing of masks, which was the main tool of the COVID-19 quarantine policy, we paid attention to the fact that some people are still wearing masks, and we wanted to clarify why people do not take off their masks. Through a survey in this regard, we were able to ascertain why some people continue to wear masks in a broader context. In this article, we directly and indirectly confirm the hidden side of citizens' continued wearing of masks by analyzing how the lifting of the mask-wearing obligation was reported in media articles that have a significant impact on citizens' behavior and attitude. Through this, it was confirmed that citizens continue to wear masks to protect themselves in an uncertain situation where the COVID-19 endemic has not been declared, despite the quarantine authorities' announcement of lifting the mandatory wearing. In a situation where crises such as COVID-19 are expected to repeat frequently in the future, it was concluded that it is important to build trust in the quarantine authorities.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.279-282
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• 2001

본 논문에서는 가상의 블루스크린(Virtual Blue Screens, VBS)을 이용한 반자동 영상분할 기법을 제안한다. 가상 블루스크린은 동영상에서 배경영역을 특정한 값으로 채워 만든 참조영상으로 정의한다. 반자동 영상 분할 기법은 크게 화면내 영상분할과 화면간 영상분할의 두 단계로 이루어진다. 화면내 영상분할은 VBS와 원영상의 형태학적 분할 기법을 사용하고, 화면간 영상 분할은 두개의 연속하는 화면에서 변화검출(Change Detection)로 이루어진다 [1]. 본 논문에서는 효과적인 변화검출을 위하여 제안된 VBS를 사용한다. VBS를 이용한 영상분할에서는 우선, 이전화면에서 만들어진 VBS를 참조하여 다음화면에서 움직임 영역을 예측한다. 이렇게 예측된 영상과 원영상에 대해 형태학적 분할 기법(Morphological Segmentation Technique)을 이용해서 각각에 대한 레이블 마스크(Label Mask)를 얻는다 [2]. 두개의 레이블 마스크 사이에는 서로 공통된 영역들이 존재하게 되는데, 이런 공통된 영역을 추출함으로써 움직임 객체를 검출한다. 현재화면에서 검출된 움직임 객체는 다음화면을 위한 가상의 블루 스크린을 만드는데 사용한다.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.