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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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樓主: 婉言
31#
發(fā)表于 2025-3-26 23:30:19 | 只看該作者
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.
32#
發(fā)表于 2025-3-27 01:34:24 | 只看該作者
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.
33#
發(fā)表于 2025-3-27 07:45:53 | 只看該作者
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.
34#
發(fā)表于 2025-3-27 12:51:00 | 只看該作者
35#
發(fā)表于 2025-3-27 14:02:31 | 只看該作者
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.
36#
發(fā)表于 2025-3-27 20:41:26 | 只看該作者
37#
發(fā)表于 2025-3-27 22:06:20 | 只看該作者
38#
發(fā)表于 2025-3-28 02:05:58 | 只看該作者
39#
發(fā)表于 2025-3-28 08:40:53 | 只看該作者
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.
40#
發(fā)表于 2025-3-28 14:29:33 | 只看該作者
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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