ScholarWorks@Soongsil University: Chung, Minyoung

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Chung, Minyoung (정민영)

College of Information Technology (School of Software)

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noise filtering:2||딥러닝:2||adversarial example detection:2||Medical image segmentation:2||Representation learning:2||frequency domain:2||Liver segmentation:2||Adversarial learning:1||Adversarial noise prediction:1||Anglebased kernel convolutions:1||Attackagnostic Defense:1||Autocontext neural network:1||Auxiliary decoder networks:1||Block matching 3D (BM3D):1||CTmodel registration:1||Cardiac CT segmentation:1||Cephalometric landmark detection:1||Computational modeling:1||Computed tomography:1||Cone beam computed tomography:1||Contour attention map:1||Contour attention network:1||Contour embedded network:1||Convolutional neural network:1||Deep Learning:1||Deep learning:1||Dental computed tomography (CT):1||Dentistry:1||Depth image enhancement:1||Direct point regression:1||Distance regularized point regression:1||Distance transformbased segmentation:1||Distancebased segmentation:1||Enhanced depth dataset:1||Explicit global and local feature fusion:1||Feature discriminator:1||Frequency domain classification:1||Gaussian disentanglement loss:1||Global context aggregation:1||Highlevel residual shape prior:1||Image segmentation:1||Individual tooth detection:1||Instance segmentation:1||Level set:1||Liver:1||Maximum intensity:1||Metal artifacts reduction:1||Multiorgan segmentation:1||Multiple weld seam extraction:1||Neural networks:1||Panoramic Xray image detection:1||Patchwise attention:1||Point cloud registration:1||Pointbased object detection:1||Pointwise object detection:1||Pose regression neural network:1||Poseaware tooth detection:1||Predictionbased adversarial detection:1||RGBD image:1||RGBdepth image:1||RGB깊이 이미지:1||Registration:1||Rotationrobust point descriptor:1||Scale adaptation:1||Scanned surface model:1||Seam extraction:1||Segmentation:1||Selfsupervised neural network:1||Selfsupervising network:1||Semisupervised learning:1||Shapeaware contour attention:1||Siamese network:1||Singular value decomposition:1||Surgery:1||Threedimensional displays:1||Tooth CBCT segmentation:1||Tooth identification:1||Tooth instance segmentation:1||Two dimensional displays:1||Unsupervised depth registration:1||Vessel:1||deep learning:1||deep pose regression neural network:1||groundtruth depth image:1||optimal clusterbased similarity:1||scanned dental model registration:1||single image depth estimation:1||supervised depth estimation:1||공격 방법에 무관한 방어:1||단일 영상 깊이 추정:1||적대적 공격:1||적대적 사례:1||정답 깊이 이미지:1||지도학습 기반 깊이 추정:1||3D point cloud analysis:1||특이값 분해:1||3D reconstruction:1||Abdominal ct segmentation:1||Active contour:1||Adversarial attack:1||Adversarial attack defense:1||Adversarial example detection:1||

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