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Titlebook: Computer Vision Systems; 10th International C Lazaros Nalpantidis,Volker Krüger,Antonios Gastera Conference proceedings 2015 Springer Inter

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楼主: Stimulant
发表于 2025-3-23 10:22:01 | 显示全部楼层
0302-9743 reed proceedings of the 10th International Conference on Computer Vision Systems, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The 48 papers presented were carefully reviewed and selected from 92 submissions. The paper are organized in topical sections on biological and cognitive vision; ha
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Uwe Frank MD, PhD,Evelina Tacconelli MD, PhDptive fields. To test our approach we compared the performance of the original FREAK and our proposal on the 15 scene categories database. The results show that our approach outperforms the original FREAK for the scene classification task.
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https://doi.org/10.1007/978-3-662-03725-6e candidates are created by successively adding segments to a seed segment in a saliency-guided way. Finally, the resulting object candidates are ranked based on Gestalt principles. We show that the proposed algorithm clearly outperforms three other recent methods for object discovery on the challenging Kitchen dataset.
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Saliency-Guided Object Candidates Based on Gestalt Principlese candidates are created by successively adding segments to a seed segment in a saliency-guided way. Finally, the resulting object candidates are ranked based on Gestalt principles. We show that the proposed algorithm clearly outperforms three other recent methods for object discovery on the challenging Kitchen dataset.
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Integers and Fixed-Point Numbersnder the hypothesis of a flat ground. The proposed architecture combines cost effectiveness, high frame-rate with low latency, low power consumption and without any prior knowledge of the scene compared to existing implementations.
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Sleep Pose Recognition in an ICU Using Multimodal Data and Environmental Feedback, and pressure. Classification results indicate that our method achieves 100 % accuracy (outperforming previous techniques by 6 %) in bright and clear (ideal) scenes, 70 % in poorly illuminated scenes, and 90 % in occluded ones.
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Real Time Vision System for Obstacle Detection and Localization on FPGAnder the hypothesis of a flat ground. The proposed architecture combines cost effectiveness, high frame-rate with low latency, low power consumption and without any prior knowledge of the scene compared to existing implementations.
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