• Journal of Sensor Science and Technology
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• v.8 no.2
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• pp.154-162
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• 1999

In this paper, we acquired EBT section images of lung parenchyma using fabricated spirometric gating device and proposed new energy function based on dynamic contour model in order to extracted the contour of the lung parenchyma in EBT images. In EBT images, gray level of the lungs is lower than other region. we extracted the lungs contour using the new energy function considering gray level and contour vector of the lung parenchyma region from EBT images. As we compared the proposed method with the conventional method, we confirmed that detection method using proposed energy function was valid.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.60-67
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• 2008

Skin color is a very important information for an automatic face recognition. In this paper, we proposed a skin region extraction method using color information and skin color model. We use the adaptive lighting compensation technique for improved performance of skin region extraction. Also, using an preprocessing filter, normally large areas of easily distinct non skin pixels, are eliminated from further processing. And we use the modified ST color space, where undesired effects are reduced and the skin color distribution fits better than others color space. Experimental results show that the proposed method has better performance than the conventional methods, and reduces processing time by $35{\sim}40%$ on average.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1647-1654
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• 2013

Detection performance of the vehicle on the road depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a vehicle detection system in conjunction with a robust background estimate algorithm to environment change on the road in daytime is proposed. Gaussian Mixture Model is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the vehicle for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithm were useful to detect vehicles in the road.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.780-785
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• 2006

HCI, 비전 기반 사용자 인터페이스 또는 제스쳐 인식과 같은 많은 분야에서 3 차원 얼굴 모션을 추정하는 것은 중요한 작업이다. 연속된 2 차원 이미지로부터 3 차원 모션을 추정하기 위한 방법으로는 크게 외형 기반 방법이나 모델을 이용하는 방법이 있다. 본 연구에서는 동영상으로부터 3 차원 실린더 모델과 Optical flow를 이용하여 실시간으로 얼굴 모션을 추정하는 방법을 제안하고자 한다. 초기 프레임으로부터 얼굴의 피부색과 템플릿 매칭을 이용하여 얼굴 영역을 검출하고 검출된 얼굴 영역에 3 차원 실린더 모델을 투영하게 된다. 연속된 프레임으로 부터 Lucas-Kanade 의 Optical flow 를 이용하여 얼굴 모션을 추정한다. 정확한 얼굴 모션 추정을 하기 위해 IRLS 방법을 이용하여 각 픽셀에 대한 가중치를 설정하게 된다. 또한, 동적 템플릿을 이용해 오랫동안 정확한 얼굴 모션 추정하는 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.153-156
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• 2007

인간의 시각 시스템은 선택적 주의 집중에 의해 시각 수용체로 도달되는 많은 물체들 중에서 필요한 정보만을 추출하여 원하는 작업을 수행한다. Itti와 Koch는 시각적 주의를 제어할 수 있는, 신경계를 모방한 계산적 모델을 제안하였으나 조명환경에 고정적인 saliency map을 구성하였다. 따라서, 본 논문에서는 영상에서 ROI(region of interest)을 탐지하기 위한 조명환경에 적응적인 saliency map 모델을 구성하는 기법을 제시한다. 변화하는 환경에서 원하는 특징을 부각시키기 위하여 상황에 적응적인 동적 가중치를 부여한다. 동적 가중치는 conspicuity map에 S.K. Chang이 제안한 PIM(Picture Information Measure)을 적용시켜 정보량을 측정한 후, 이에 따라 정규화된 값을 부여함으로써 구현한다. 제안하는 조명환경에 강인한 적응적인 saliency map 모델 구현의 성능을 얼굴검출 실험을 통하여 검증하였다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.426-428
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• 2022

화재 상황에서의 빠른 현장 파악은 인명피해를 줄이는데 중요한 요소이다. 기존 연구의 화재와 관련된 데이터셋들은 대부분 불과 연기를 라벨링하여 화재의 예방에 초점을 두고 있다. 본 연구에서는 화재 상황에서 사람과 소방관, 연기, 불을 탐지하는 Object detection 모델을 만들어 현장 파악에 더욱 도움을 주고자 하였다. 이를 위해 화재 상황 이미지 약 3000장을 수집하고 라벨링하여 데이터셋을 구성하였으며 이를 이용해 객체 검출 모델인 RetinaNet을 학습하였다. 또한, 화재 상황에서 Object Detection 모델의 성능을 향상시키기 위해 기존 모델인 RetinaNet에 Dehazing(FFA-Net), Smoke augmentation, semi-supervised(ISD) 방법을 적용하였고, semi-supervised 조건에서 mAP 63.7로 가장 높은 성능을 도출하였다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.6
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• pp.322-332
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• 2014

This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.168-176
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• 2014

Detecting moving objects from a video sequence is a fundamental and critical task in video surveillance, traffic monitoring and analysis, and human detection and tracking. It is very difficult to detect moving objects in a video sequence degraded by the environmental factor such as fog. In particular, the color of an object become similar to the neighbor and it reduces the saturation, thus making it very difficult to distinguish the object from the background. For such a reason, it is shown that the performance and reliability of object detection and tracking are poor in the foggy weather. In this paper, we propose a novel method to improve the performance of object detection, combining a haze removal algorithm and a local histogram-based object tracking method. For the quantitative evaluation of the proposed system, information retrieval measurements, recall and precision, are used to quantify how well the performance is improved before and after the haze removal. As a result, the visibility of the image is enhanced and the performance of objects detection is improved.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.672-679
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• 2011

In this paper, detection algorithm of face region based on skin color of in the TV images is proposed. In the first, reference image is set to the sampled skin color, and then the extracted of face region is candidated using the Euclidean distance between the pixels of TV image. The eye image is detected by using the mean value and standard deviation of the component forming color difference between Y and C through the conversion of RGB color into CMY color model. Detecting the lips image is calculated by utilizing Q component through the conversion of RGB color model into YIQ color space. The detection of the face region is extracted using basis of knowledge by doing logical calculation of the eye image and lips image. To testify the proposed method, some experiments are performed using front color image down loaded from TV color image. Experimental results showed that face region can be detected in both case of the irrespective location & size of the human face.