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Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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發(fā)表于 2025-3-21 16:48:49 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision – ECCV 2022 Workshops
副標(biāo)題Tel Aviv, Israel, Oc
編輯Leonid Karlinsky,Tomer Michaeli,Ko Nishino
視頻videohttp://file.papertrans.cn/235/234288/234288.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit
描述The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online..The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows:..Part I:. W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision..Part II:. W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation;..Part III:. W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?;..Part IV:. W10 - Self-Supervised Learning for Next-Generation Industry-LevelAutonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; computer networks; computer vision; data security; face recognition; gesture rec
版次1
doihttps://doi.org/10.1007/978-3-031-25056-9
isbn_softcover978-3-031-25055-2
isbn_ebook978-3-031-25056-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-031-25056-9artificial intelligence; computer networks; computer vision; data security; face recognition; gesture rec
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Computer Vision – ECCV 2022 Workshops978-3-031-25056-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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The Effects of Noise on Aquatic Lifeontact (TTC) with the surface during ventral landing. This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing. Our core contributions are a novel contrast maximisation formulation for event-based divergence estim
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Arthur N. Popper,Anthony D. Hawkinsr space-based EO systems. Since the development of on-orbit AI applications requires rarely available raw data, training of these AI networks remains a challenge. To address this issue, we investigate the effects of varying two key image parameters between training and testing data on a ship segment
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發(fā)表于 2025-3-22 18:13:38 | 只看該作者
Alexander S. Douglass,Shima Abadilgorithms for pose estimation employ data-driven techniques. However, there is an absence of real training data for spacecraft imaged in space conditions due to the costs and difficulties associated with the space environment. This has motivated the introduction of 3D data simulators, solving the is
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Arthur N. Popper,Anthony Hawkins cameras. The idea is to generate a 3D scene of Mars’ surface to address key challenges in planetary surface exploration such as: planetary geology, simulated navigation and shape analysis. Although there exist different methods to enable a 3D reconstruction of Mars’ surface, they rely on classical
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發(fā)表于 2025-3-23 03:41:31 | 只看該作者
Hearing, Noise, and Echolocating Odontocetesbased telescopes. Event cameras provide high-dynamic range, high temporal resolution, low latency asynchronous “event” data that captures illumination changes in a scene. Such data is ideally suited for estimating star motion since it has minimal image blur and can capture low-intensity changes in i
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發(fā)表于 2025-3-23 07:35:55 | 只看該作者
Arthur N. Popper,Anthony Hawkinsal resolution of multiple images containing the same target. Increasing the resolution of images through super-resolution (SR) techniques can improve the performance of commodity optical hardware, allowing more science to be done with cheaper equipment. Many SR techniques rely on the accurate regist
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