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Titlebook: Image and Graphics; 11th International C Yuxin Peng,Shi-Min Hu,Kun Xu Conference proceedings 2021 Springer Nature Switzerland AG 2021 artif

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樓主: DEIFY
51#
發(fā)表于 2025-3-30 09:58:41 | 只看該作者
52#
發(fā)表于 2025-3-30 13:22:14 | 只看該作者
Novel Augmented Reality System for Oral and Maxillofacial Surgerywever, the current augmented reality methods need to develop personalized occlusal splints and perform secondary Computed Tomography (CT) scanning. These unnecessary preparations lead to high cost and extend the time of preoperative preparation. In this paper, we propose an augmented reality surgery
53#
發(fā)表于 2025-3-30 18:53:35 | 只看該作者
A New Dataset and Recognition for Egocentric Microgesture Designed by Ergonomistsrfaces has an impact on usability, comfort, and efficiency. Hand controllers and gestures are popularly used in VR/AR devices. However, users may suffer from overloading on the upper extremities while raising the hand or controller. Therefore, we released a microgesture library with 19 microgestures
54#
發(fā)表于 2025-3-30 22:59:49 | 只看該作者
55#
發(fā)表于 2025-3-31 00:59:37 | 只看該作者
Robust Recovery of Low Rank Matrix by Nonconvex Rank Regularizationts a biased estimator when nuclear norm relaxes the rank function. To solve this issue, we focus on studying nonconvex rank regularization problems for both robust matrix completion (RMC) and low rank representation (LRR), respectively. By extending both to a general low rank matrix minimization pro
56#
發(fā)表于 2025-3-31 05:53:44 | 只看該作者
Free Adversarial Training with Layerwise Heuristic Learning enhances robustness of DNN-based systems by augmenting training data with adversarial samples. Projected gradient descent adversarial training (PGD AT), one of the promising defense methods, can resist strong attacks. We propose “free” adversarial training with layerwise heuristic learning (.) to r
57#
發(fā)表于 2025-3-31 11:13:33 | 只看該作者
58#
發(fā)表于 2025-3-31 14:52:54 | 只看該作者
59#
發(fā)表于 2025-3-31 19:03:50 | 只看該作者
An LDA and RBF-SVM Based Classification Method for Inertinite Macerals of Coalpport vector machine (SVM) is proposed. Firstly, according to differences of texture and intensity among macerals, inertinite macerals are represented with texture related features as energy, entropy, moment, local smooth and intensity related features as contrast, mean, standard deviation, 3-order
60#
發(fā)表于 2025-3-31 23:34:54 | 只看該作者
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