{"product_id":"computer-vision-eccv-2022-17th-european-conference-tel-aviv-israel-october-2327-2022-proceedings-part-xxxii-9783031198236","title":"Computer Vision - ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXII","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThe 39-volume set, consisting of the LNCS books 13661 to 13699, contains the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The papers cover various topics in computer vision, machine learning, deep neural networks, and more, with 1645 papers selected from 5804 submissions. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 741 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 12 November 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer International Publishing AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThe 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision (ECCV) 2022, held in Tel Aviv, Israel, during October 23-27, 2022.\u003cbr\u003e\u003cbr\u003eThe 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers cover a wide range of topics in computer vision, including:\u003cbr\u003e\u003cbr\u003eComputer Vision: This field encompasses the development and application of algorithms for tasks such as image analysis, object detection, and scene understanding.\u003cbr\u003e\u003cbr\u003eMachine Learning: Machine learning algorithms are used to analyze and interpret data, enabling computers to make predictions and decisions based on patterns.\u003cbr\u003e\u003cbr\u003eDeep Neural Networks: Deep neural networks are a powerful tool for analyzing and processing complex data, particularly in areas such as image recognition and natural language processing.\u003cbr\u003e\u003cbr\u003eReinforcement Learning: Reinforcement learning involves training agents to make decisions in complex environments by receiving feedback in the form of rewards or penalties.\u003cbr\u003e\u003cbr\u003eObject Recognition: Object recognition algorithms are used to identify and classify objects in images and videos, enabling applications such as autonomous vehicles and facial recognition systems.\u003cbr\u003e\u003cbr\u003eImage Classification: Image classification algorithms are used to categorize images based on their content, such as flowers, animals, or buildings.\u003cbr\u003e\u003cbr\u003eImage Processing: Image processing algorithms are used to enhance, modify, and analyze images, enabling tasks such as image restoration and image compression.\u003cbr\u003e\u003cbr\u003eObject Detection: Object detection algorithms are used to identify and locate objects in images and videos, enabling applications such as security systems and autonomous vehicles.\u003cbr\u003e\u003cbr\u003eSemantic Segmentation: Semantic segmentation algorithms are used to divide an image into different regions based on their semantic meaning, such as objects, backgrounds, or faces.\u003cbr\u003e\u003cbr\u003eHuman Pose Estimation: Human pose estimation algorithms are used to estimate the pose of a human body in images and videos, enabling applications such as virtual reality and human-computer interaction.\u003cbr\u003e\u003cbr\u003eThree-Dimensional Reconstruction: Three-dimensional reconstruction algorithms are used to create three-dimensional models of objects and scenes from two-dimensional images and videos.\u003cbr\u003e\u003cbr\u003eStereo Vision: Stereo vision algorithms are used to create depth perception and 3D reconstructions from multiple camera images.\u003cbr\u003e\u003cbr\u003eComputational Photography: Computational photography algorithms are used to enhance and manipulate images and videos using computer vision techniques.\u003cbr\u003e\u003cbr\u003eNeural Networks: Neural networks are a class of algorithms that are inspired by the structure and function of the human brain. They are used for a wide range of tasks, including image recognition, natural language processing, and machine learning.\u003cbr\u003e\u003cbr\u003eImage Coding: Image coding algorithms are used to reduce the size of images while maintaining their quality, enabling efficient storage and transmission of images over networks.\u003cbr\u003e\u003cbr\u003eImage Reconstruction: Image reconstruction algorithms are used to recreate images from incomplete or corrupted data, enabling tasks such as medical imaging and satellite imaging.\u003cbr\u003e\u003cbr\u003eObject Recognition: Object recognition algorithms are used to identify and classify objects in images and videos, enabling applications such as autonomous vehicles and facial recognition systems.\u003cbr\u003e\u003cbr\u003eMotion Estimation: Motion estimation algorithms are used to estimate the motion of objects in images and videos, enabling applications such as video surveillance and sports analysis.\u003cbr\u003e\u003cbr\u003eThese proceedings represent the latest advancements in computer vision research and technology, and they will be valuable to researchers, practitioners, and students in the field.\u003cbr\u003e\u003cbr\u003eThe 17th European Conference on Computer Vision (ECCV) 2022, held in Tel Aviv, Israel, from October 23-27, 2022, witnessed the presentation of a remarkable 1645 papers. These papers, meticulously reviewed and selected from a staggering 5804 submissions, cover a diverse range of topics within the field of computer vision.\u003cbr\u003e\u003cbr\u003eComputer vision, a multidisciplinary domain, encompasses a wide array of techniques and algorithms aimed at understanding and interpreting visual data. The papers presented at ECCV 2022 delve into various aspects of computer vision, including:\u003cbr\u003e\u003cbr\u003eComputer Vision: This overarching field encompasses the development and application of algorithms for tasks such as image analysis, object detection, and scene understanding. Researchers explore innovative methods to enhance the accuracy, efficiency, and robustness of computer vision systems, enabling them to perform tasks in diverse environments and applications.\u003cbr\u003e\u003cbr\u003eMachine Learning: Machine learning algorithms play a pivotal role in computer vision. These algorithms are trained on large datasets to learn patterns and make predictions based on the data. They are widely used in various applications, including image recognition, object detection, and facial recognition.\u003cbr\u003e\u003cbr\u003eDeep Neural Networks: Deep neural networks, a subset of machine learning, are characterized by their ability to learn complex patterns and make predictions with high accuracy. They are extensively used in image recognition, object detection, and facial recognition tasks.\u003cbr\u003e\u003cbr\u003eReinforcement Learning: Reinforcement learning involves training agents to make decisions in complex environments by receiving feedback in the form of rewards or penalties. This approach is particularly useful in autonomous vehicles, robotics, and game playing.\u003cbr\u003e\u003cbr\u003eObject Recognition: Object recognition algorithms are employed to identify and classify objects in images and videos. These algorithms are crucial in various applications, such as autonomous vehicles, surveillance systems, and medical imaging.\u003cbr\u003e\u003cbr\u003eImage Classification: Image classification algorithms categorize images based on their content, such as flowers, animals, or buildings. These algorithms are widely used in image retrieval, content-based indexing, and image analysis.\u003cbr\u003e\u003cbr\u003eImage Processing: Image processing algorithms are used to enhance, modify, and analyze images. These algorithms are employed in various applications, including image restoration, image compression, and image segmentation.\u003cbr\u003e\u003cbr\u003eObject Detection: Object detection algorithms are employed to identify and locate objects in images and videos. These algorithms are crucial in various applications, such as security systems, autonomous vehicles, and medical imaging.\u003cbr\u003e\u003cbr\u003eSemantic Segmentation: Semantic segmentation algorithms are used to divide an image into different regions based on their semantic meaning, such as objects, backgrounds, or faces. These algorithms are widely used in medical imaging, autonomous vehicles, and scene understanding.\u003cbr\u003e\u003cbr\u003eHuman Pose Estimation: Human pose estimation algorithms are employed to estimate the pose of a human body in images and videos. These algorithms are crucial in various applications, such as virtual reality, human-computer interaction, and sports analysis.\u003cbr\u003e\u003cbr\u003eThree-Dimensional Reconstruction: Three-dimensional reconstruction algorithms are used to create three-dimensional models of objects and scenes from two-dimensional images and videos. These algorithms are widely used in computer graphics, archaeology, and virtual reality.\u003cbr\u003e\u003cbr\u003eStereo Vision: Stereo vision algorithms are used to create depth perception and 3D reconstructions from multiple camera images. These algorithms are widely used in autonomous vehicles, robotics, and augmented reality.\u003cbr\u003e\u003cbr\u003eComputational Photography: Computational photography algorithms are used to enhance and manipulate images and videos using computer vision techniques. These algorithms are widely used in photography, video editing, and content creation.\u003cbr\u003e\u003cbr\u003eNeural Networks: Neural networks are a class of algorithms inspired by the structure and function of the human brain. They are widely used in various applications, including image recognition, natural language processing, and machine learning.\u003cbr\u003e\u003cbr\u003eImage Coding: Image coding algorithms are used to reduce the size of images while maintaining their quality. These algorithms are crucial in various applications, such as image transmission, storage, and retrieval.\u003cbr\u003e\u003cbr\u003eImage Reconstruction: Image reconstruction algorithms are used to recreate images from incomplete or corrupted data. These algorithms are widely used in medical imaging, satellite imaging, and remote sensing.\u003cbr\u003e\u003cbr\u003eObject Recognition: Object recognition algorithms are used to identify and classify objects in images and videos. These algorithms are crucial in various applications, such as autonomous vehicles, surveillance systems, and facial recognition.\u003cbr\u003e\u003cbr\u003eMotion Estimation: Motion estimation algorithms are used to estimate the motion of objects in images and videos. These algorithms are widely used in video surveillance, sports analysis, and robotics.\u003cbr\u003e\u003cbr\u003eThe 17th European Conference on Computer Vision (ECCV) 2022 serves as a platform for researchers, practitioners, and students to exchange ideas, share their findings, and collaborate on advancing the state-of-the-art in computer vision. The high-quality papers presented at the conference highlight the significant contributions made by the global computer vision community, and they will undoubtedly have a profound impact on the future of this field.\u003cbr\u003e\u003cbr\u003eIn conclusion, the 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision (ECCV) 2022, held in Tel Aviv, Israel, during October 23-27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions, covering a wide range of topics in computer vision. The papers cover a diverse range of topics, including computer vision, machine learning, deep neural networks, reinforcement learning, object recognition, image classification, image processing, object detection, semantic segmentation, human pose estimation, 3d reconstruction, stereo vision, computational photography, neural networks, image coding, image reconstruction, object recognition, motion estimation, and more. The proceedings represent the latest advancements in computer vision research and technology, and they will be valuable to researchers, practitioners, and students in the field.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 1205g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783031198236\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Paperback \/ softback","offer_id":45290376560890,"sku":"9783031198236","price":85.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_7856e178-ed56-429d-bc37-dc8cb095f548.jpg?v=1706343391","url":"https:\/\/shulphink.com\/products\/computer-vision-eccv-2022-17th-european-conference-tel-aviv-israel-october-2327-2022-proceedings-part-xxxii-9783031198236","provider":"Shulph Ink","version":"1.0","type":"link"}