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Titlebook: New Approaches for Multidimensional Signal Processing; Proceedings of Inter Roumen Kountchev,Rumen Mironov,Kazumi Nakamatsu Conference proc

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21#
發(fā)表于 2025-3-25 04:15:48 | 只看該作者
22#
發(fā)表于 2025-3-25 08:33:08 | 只看該作者
Object Motion Detection in Video by Fusion of RPCA and NMF DecompositionsMatrix Factorization with the aim of detecting moving objects over stationary background. The schemes use the logical OR and AND operators on a pixel basis over the binary outputs of the base decomposition algorithms. Experimental results from testing with videos, containing natural scenes with huma
23#
發(fā)表于 2025-3-25 13:20:21 | 只看該作者
24#
發(fā)表于 2025-3-25 19:07:15 | 只看該作者
25#
發(fā)表于 2025-3-25 20:03:57 | 只看該作者
SIFT Based Feature Matching Algorithm for Cartoon Plagiarism Detectiont of cartoon images has also become a major obstacle to its development. The theoretical defects of the current law, the concealment of infringement forms, and the low cost of infringement are the main reasons for this dilemma. With the rapid development of Internet information and digital image pro
26#
發(fā)表于 2025-3-26 02:20:05 | 只看該作者
Image Recognition Technology Based Evaluation Index of Ship Navigation Risk in Bridge Areaays. Therefore, the risk of ship accidents in inland waterways is increasing year by year. This paper mainly studies the research and application of the navigation safety risk evaluation index system in bridge area based on image recognition technology. The convolutional neural network based detecti
27#
發(fā)表于 2025-3-26 06:45:48 | 只看該作者
28#
發(fā)表于 2025-3-26 11:29:25 | 只看該作者
Small Object Detection of Remote Sensing Images Based on Residual Branch of Feature Fusioning small object detection methods fuse the multi-scale features of different layers directly when using the feature pyramid network. However, due to the decrease of channels in feature fusion, the top-level feature of pyramid will lose information of the object, which is disadvantageous to detect s
29#
發(fā)表于 2025-3-26 13:04:53 | 只看該作者
Meta-learning with Logistic Regression for Multi-classificationression, and linear support vector machine. This study introduces a nonlinear basic learner-logistic regression to improve meta-learning through fast convergence in learning downstream tasks and obtaining the global optimal solution. The Woodbury identity is utilized to express our advantages in a s
30#
發(fā)表于 2025-3-26 17:45:00 | 只看該作者
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