• 한국컴퓨터정보학회논문지
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• 제19권8호
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• pp.27-34
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• 2014

이 논문에서는 hybrid projection 함수와 rank order 필터를 이용하여 눈동자를 검출하는 방법을 제안한다. 눈썹을 눈동자로 검출하는 오류를 줄이기 위하여, hybrid projection 함수를 이용하여 얼굴 영역에서 눈썹을 검출하고 눈썹이 포함되지 않도록 눈 영역을 설정한다. 눈 영역에서 rank order 필터를 사용하여 눈동자 후보점을 찾고 위치를 보정한다. 두 눈동자 후보점을 기하학적인 제약조건을 기반으로 쌍으로 묶고 각 쌍의 두 눈에 대한 유사도를 정합을 이용하여 측정하여가장작은값을 갖는 쌍을 최종눈동자로 결정한다. BioID 얼굴데이터베이스의 얼굴 영상 700개에 대한 실험 결과 92.4%의 검출율을 얻었으며 기존 방법보다 약 21.5% 개선된 결과를 얻었다.

• Journal of applied mathematics & informatics
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• 제12권1_2호
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• pp.199-209
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• 2003

When a sampling theorem holds in wavelet subspaces, sampling expansions can be a good approximation to projection expansions. Even when the sampling theorem does not hold, the scaling function series with the usual coefficients replaced by sampled function values may also be a good approximation to the projection. We refer to such series as hybrid sampling series. For this series, we shall investigate the local convergence and analyze Gibbs phenomenon.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Korean Journal of Radiology
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• 제19권6호
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• pp.1187-1195
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• 2018

Objective: To compare correlations between pulmonary function test (PFT) results and different reconstruction algorithms and to suggest the optimal reconstruction protocol for computed tomography (CT) quantification of low lung attenuation areas and airways in healthy individuals. Materials and Methods: A total of 259 subjects with normal PFT and chest CT results were included. CT scans were reconstructed using filtered back projection, hybrid-iterative reconstruction, and model-based IR (MIR). For quantitative analysis, the emphysema index (EI) and wall area percentage (WA%) were determined. Subgroup analysis according to smoking history was also performed. Results: The EIs of all the reconstruction algorithms correlated significantly with the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) (all p < 0.001). The EI of MIR showed the strongest correlation with FEV1/FVC (r = -0.437). WA% showed a significant correlation with FEV1 in all the reconstruction algorithms (all p < 0.05) correlated significantly with FEV1/FVC for MIR only (p < 0.001). The WA% of MIR showed the strongest correlations with FEV1 (r = -0.205) and FEV1/FVC (r = -0.250). In subgroup analysis, the EI of MIR had the strongest correlation with PFT in both eversmoker and never-smoker subgroups, although there was no significant difference in the EI between the reconstruction algorithms. WA% of MIR showed a significantly thinner airway thickness than the other algorithms ($49.7{\pm}7.6$ in ever-smokers and $49.5{\pm}7.5$ in never-smokers, all p < 0.001), and also showed the strongest correlation with PFT in both ever-smoker and never-smoker subgroups. Conclusion: CT quantification of low lung attenuation areas and airways by means of MIR showed the strongest correlation with PFT results among the algorithms used, in normal subjects.