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41#
發(fā)表于 2025-3-28 15:48:29 | 只看該作者
42#
發(fā)表于 2025-3-28 22:07:13 | 只看該作者
https://doi.org/10.1007/978-1-349-08149-3r of vertices decrease the operator time, which reduces the cost of processing the data. A?viable solution to finding a polyline within a specified tolerance with the minimum number of vertices is described in this paper.
43#
發(fā)表于 2025-3-29 00:59:19 | 只看該作者
Multilateralism and Western Strategytrieve information from images depicting molecular pathway diagrams. The lack of a significant, publicly available dataset with annotated ground truth has led to experimental evaluation on synthetic data. Results show high precision and recall values for the detection of entities and relations. We c
44#
發(fā)表于 2025-3-29 06:47:41 | 只看該作者
https://doi.org/10.1007/978-3-030-75718-2es such as MSE, precision, accuracy and recall.The ionization data of the February 1956 GLE event was then extracted from the ionization chamber recordings and converted to percentage increase above background cosmic ray levels, for comparison to existing neutron monitor data which was sourced from
45#
發(fā)表于 2025-3-29 07:53:29 | 只看該作者
https://doi.org/10.1007/978-3-663-04312-6rs trace symbols using an e-pen over a digital surface, which provides both the underlying image (offline data) and the drawing made (online data). Using both sources, the system is capable of reaching an error below 4% when recognizing the symbols with a Convolutional Neural Network.
46#
發(fā)表于 2025-3-29 12:15:28 | 只看該作者
https://doi.org/10.1007/978-3-642-31776-7nal approach using tree of connected components for the separation of the content in layers for facilitating the extraction, the analysis, the viewing and the diffusion of the data contained in these ancient linguistic atlases.
47#
發(fā)表于 2025-3-29 17:49:13 | 只看該作者
48#
發(fā)表于 2025-3-29 22:50:47 | 只看該作者
Pen-Based Music Document Transcription with Convolutional Neural Networksrs trace symbols using an e-pen over a digital surface, which provides both the underlying image (offline data) and the drawing made (online data). Using both sources, the system is capable of reaching an error below 4% when recognizing the symbols with a Convolutional Neural Network.
49#
發(fā)表于 2025-3-30 01:50:13 | 只看該作者
Extraction of Ancient Map Contents Using Trees of Connected Componentsnal approach using tree of connected components for the separation of the content in layers for facilitating the extraction, the analysis, the viewing and the diffusion of the data contained in these ancient linguistic atlases.
50#
發(fā)表于 2025-3-30 07:19:54 | 只看該作者
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