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깊이정보 카메라 및 다시점 영상으로부터의 다중깊이맵 융합기법
엄기문(Gi-Mun Um),안충현(Chunghyun Ahn),이수인(Soo In Lee),김강연(Kang Yeon Kim),이관행(Kwan H. Lee) 한국방송·미디어공학회 2004 방송공학회논문지 Vol.9 No.3
This paper presents a multi-depth map fusion method for the 3D scene reconstruction. It fuses depth maps obtained from the stereo matching technique and the depth camera. Traditional stereo matching techniques that estimate disparities between two images often produce inaccurate depth map because of occlusion and homogeneous area. Depth map obtained from the depth camera is globally accurate but noisy and provide a limited depth range. In order to get better depth estimates than these two conventional techniques, we propose a depth map fusion method that fuses the multi-depth maps from stereo matching and the depth camera. We first obtain two depth maps generated from the stereo matching of 3-view images. Moreover, a depth map is obtained from the depth camera for the center-view image. After preprocessing each depth map, we select a depth value for each pixel among them. Simulation results showed a few improvements in some background regions by proposed fusion technique.
위성 영상 분류를 위한 규칙 기반 훈련 집합 선택에 관한 연구
엄기문(Um Gi Mun),이쾌희(Lee Kwae Hi) 한국정보처리학회 1996 정보처리학회논문지 Vol.3 No.7
The conventional training set selection methods for the satellite image classification usually depend on the manual selection using data from the direct measurements of the ground or the ground map. However this task takes much time and cost, and some feature values vary in wide ranges even if they are in the same class. Such feature values can increase the robustness of the neural net but learning time becomes linger. In this paper, we propose a new training set selection algorithm using a rule-based method. By the technique proposed, the SPOT multispectral Imagery is classified in 3 bands, and the pixels which satisfy the rule are employed as the training sets for the neural net classifier. The experimental results show faster initial covergence and almost the same or better classification accuracy. We also showed an improvement of the classification accuracy by using texture features and NDVI.
가중 평균 기반 가상시점의 물체 표면 확률 계산을 이용한 가상시점 영상 화질 개선 연구
엄기문(Gi-Mun Um),이민재(Min-Jae Lee),이진환(Jinhwan Lee),정원식(Won-Sik Cheong),박순용(Soon-Yong Park) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.11
We propose a novel object surface probability volume integration technique by weighted averaging of object surface probability volume for each camera view when we synthesize a virtual view image. We apply the proposed technique to generate refined object surface probability volumes at the virtual view positions. In the virtual view synthesis experiment using the video sequence captured from 4 x 4 camera array, the results of the proposed technique show clearer and sharper image quality compared to that of conventional technique.