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Chapter and Conference Paper
Local Graph Fusion of Multi-view MR Images for Knee Osteoarthritis Diagnosis
Magnetic resonance imaging (MRI) has become necessary in clinical diagnosis for knee osteoarthritis (OA), while deep neural networks can contribute to the computer-assisted diagnosis. Recent works prove that i...
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Chapter and Conference Paper
Part-Wise Topology Graph Convolutional Network for Skeleton-Based Action Recognition
Action recognition based on skeleton data has attracted extensive attention in computer vision. Graph convolutional network (GCN) has achieved remarkable performance by modeling the human skeleton as a spatial...
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Chapter and Conference Paper
Semantic-Based Road Segmentation for High-Definition Map Construction
The development of autonomous driving technology proposes higher requirements of the fidelity of the high-definition maps (HD maps). The construction of HD map based on orthophotos generated from panoramic ima...
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Chapter and Conference Paper
Boundary Extraction of Planar Segments from Clouds of Unorganised Points
Planar segment detection in 3D point clouds is of importance for 3D registration, segmentation or analysis. General methods for planarity detection just detect a planar segment and label the 3D points; a bound...
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Chapter and Conference Paper
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is however still challenging, due to ...
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Chapter and Conference Paper
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers...
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Chapter and Conference Paper
Automated Tuning in Parallel Sorting on Multi-core Architectures
Empirical search is an emerging strategy used in systems like ATLAS, FFTW and SPIRAL to find the parameter values of the implementation that deliver near-optimal performance for a particular machine. However, ...