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Titlebook: Advances in Visual Computing; 10th International S George Bebis,Richard Boyle,Mark Carlson Conference proceedings 2014 Springer Internation

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楼主: 婉言
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Consensus for Decomposable Measuresfall detection benchmark dataset containing 60 occluded falls for which the end of the fall is completely occluded. We also evaluate four existing fall detection methods using a single depth camera [1–4] on this benchmark dataset.
发表于 2025-3-27 01:34:24 | 显示全部楼层
Conference proceedings 2014s systems, medical imaging, tracking for human activity monitoring, intelligent transportation systems, visual perception and robotic systems. Part II (LNCS 8888) comprises topics such as computational bioimaging , recognition, computer vision, applications, face processing and recognition, virtual reality, and the poster sessions.
发表于 2025-3-27 07:45:53 | 显示全部楼层
Vikram Gulati,Anil K. Maheshwarih based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.
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Consensus versus Dichotomous Votingerimental validation on synthetic and real-world imagery datasets and demonstrated better performance of the cascade tree-based model over the original grid-structured CRF model with loopy belief propagation inference.
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Fast Mesh-Based Medical Image Registrationh based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.
发表于 2025-3-28 14:29:33 | 显示全部楼层
One-Shot Learning of Sketch Categories with Co-regularized Sparse Codinggories and transfer them to unseen categories. We contribute a new dataset consisting of 7,760 human segmented sketches from 97 object categories. Extensive experiments reveal that the proposed method can classify unseen sketch categories given just one training sample with a 33.04% accuracy, offering a two-fold improvement over baselines.
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