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Titlebook: Gesture Recognition; Sergio Escalera,Isabelle Guyon,Vassilis Athitsos Book 2017 Springer International Publishing AG 2017 Artificial intel

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樓主: 搖尾乞憐
41#
發(fā)表于 2025-3-28 16:37:02 | 只看該作者
42#
發(fā)表于 2025-3-28 21:04:37 | 只看該作者
43#
發(fā)表于 2025-3-28 23:02:21 | 只看該作者
Qing Zhu,Xiaoxia Yang,Haifeng Listing to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.
44#
發(fā)表于 2025-3-29 07:06:28 | 只看該作者
45#
發(fā)表于 2025-3-29 09:44:08 | 只看該作者
2520-131X methods for gesture recognition.Presents an open-source C++.This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision
46#
發(fā)表于 2025-3-29 13:05:03 | 只看該作者
GEO SAR System Analysis and Design,exibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting.
47#
發(fā)表于 2025-3-29 17:46:50 | 只看該作者
P. Krishna Krishnamurthy,L. Krishnamurthyilable several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
48#
發(fā)表于 2025-3-29 22:13:51 | 只看該作者
Cumulative Distribution Estimators,together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository.
49#
發(fā)表于 2025-3-30 03:50:45 | 只看該作者
https://doi.org/10.1007/978-3-642-58567-8e manifold structure of the feature space. We show that both of them are important to achieve higher accuracy. Our experiments demonstrate improvements over traditional decision forests in the ChaLearn Gesture Challenge and MNIST data set. They also compare favorably against other state-of-the-art classifiers.
50#
發(fā)表于 2025-3-30 04:03:40 | 只看該作者
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