Computer Vision - ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVI

The 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.

Format: Paperback / softback
Length: 755 pages
Publication date: 29 October 2022
Publisher: Springer International Publishing AG

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. The papers cover a wide range of topics in computer vision, including:

Computer Vision: This field encompasses the development and application of algorithms for tasks such as image analysis, object detection, and scene understanding.

Machine Learning: Machine learning algorithms are used to analyze and interpret data, enabling computers to make predictions and decisions based on patterns.

Deep 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.

Reinforcement Learning: Reinforcement learning involves training agents to make decisions in environments that are not fully observable, and rewards them based on their performance.

Object Recognition: Object recognition algorithms are used to identify and classify objects in images and videos.

Image Classification: Image classification algorithms are used to categorize images based on their content, such as objects, landscapes, or animals.

Image Processing: Image processing algorithms are used to enhance, modify, and analyze images, such as cropping, resizing, and color correction.

Object Detection: Object detection algorithms are used to identify and locate objects in images and videos.

Semantic Segmentation: Semantic segmentation algorithms are used to divide an image into different regions based on their semantic meaning, such as objects, backgrounds, or textures.

Human Pose Estimation: Human pose estimation algorithms are used to determine the position and orientation of human bodies in images and videos.

Three-D Reconstruction: Three-D reconstruction algorithms are used to create three-dimensional models of objects and scenes from two-dimensional images.

Stereo Vision: Stereo vision algorithms are used to create depth perception and 3D reconstructions from multiple camera images.

Computational Photography: Computational photography algorithms are used to enhance and manipulate images captured by cameras.

Neural Networks: Neural networks are a type of artificial intelligence that mimics the behavior of the human brain. They are used in a wide range of applications, including image recognition, speech recognition, and natural language processing.

Image Coding: Image coding algorithms are used to compress and encode images for storage and transmission.

Image Reconstruction: Image reconstruction algorithms are used to recreate images from degraded or incomplete data.

Object Recognition: Object recognition algorithms are used to identify and classify objects in images and videos.

Motion Estimation: Motion estimation algorithms are used to estimate the motion of objects in images and videos.

These proceedings represent the latest advancements in computer vision research and provide a valuable resource for researchers, practitioners, and students in the field.

The 17th European Conference on Computer Vision (ECCV) 2022, held in Tel Aviv, Israel, from October 23 to 27, 2022, witnessed the presentation of a remarkable 1645 papers. These papers, meticulously reviewed and selected from a staggering 5804 submissions, showcased a diverse range of topics within the field of computer vision.

Computer 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 covered a broad spectrum of topics, including:

Computer Vision: This overarching field encompasses the development and application of algorithms for tasks such as image analysis, object detection, and scene understanding. Researchers explored innovative approaches to tackle challenging problems in computer vision, such as autonomous driving, medical imaging, and surveillance.

Machine Learning: Machine learning algorithms played a pivotal role in many of the presented papers. These algorithms are used to analyze and interpret data, enabling computers to make predictions and decisions based on patterns. Researchers explored various machine learning techniques, including deep learning, convolutional neural networks, and reinforcement learning, to address diverse computer vision tasks.

Deep Neural Networks: Deep neural networks, a powerful tool for analyzing and processing complex data, were extensively explored at ECCV 2022. Researchers developed and improved deep learning models for tasks such as image recognition, object detection, and facial recognition. These models demonstrated remarkable performance and opened up new avenues for solving complex vision problems.

Reinforcement Learning: Reinforcement learning, a technique that trains agents to make decisions in environments that are not fully observable, received significant attention at ECCV 2022. Researchers developed and applied reinforcement learning algorithms to solve problems in robotics, autonomous navigation, and game playing. These algorithms demonstrated the ability to learn from experience and improve performance over time.

Object Recognition: Object recognition algorithms were a central focus of many papers presented at ECCV 2022. Researchers developed and improved state-of-the-art models for identifying and classifying objects in images and videos. These models leveraged advanced techniques such as convolutional neural networks, deep learning, and transfer learning to achieve high accuracy and efficiency.

Image Classification: Image classification algorithms were used to categorize images based on their content, such as objects, landscapes, or animals. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve accurate and robust image classification.

Image Processing: Image processing algorithms were employed to enhance, modify, and analyze images. These algorithms included techniques such as image enhancement, image restoration, and image segmentation. Researchers developed and applied these algorithms to solve problems in various domains, such as medical imaging, surveillance, and computer vision.

Object Detection: Object detection algorithms were used to identify and locate objects in images and videos. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve high accuracy and efficiency in object detection tasks.

Semantic Segmentation: Semantic segmentation algorithms were used to divide an image into different regions based on their semantic meaning, such as objects, backgrounds, or textures. Researchers developed and applied these algorithms to solve problems in areas such as autonomous driving, medical imaging, and scene understanding.

Human Pose Estimation: Human pose estimation algorithms were used to determine the position and orientation of human bodies in images and videos. Researchers explored various techniques, including deep learning, convolutional neural networks, and transfer learning, to achieve accurate and robust pose estimation.

Three-D Reconstruction: Three-D reconstruction algorithms were used to create three-dimensional models of objects and scenes from two-dimensional images. Researchers developed and applied these algorithms to solve problems in areas such as archaeology, engineering, and computer vision.

Stereo Vision: Stereo vision algorithms were used to create depth perception and 3D reconstructions from multiple camera images. Researchers explored various techniques, including disparity estimation, feature matching, and stereo matching, to achieve high accuracy and efficiency in stereo vision tasks.

Computational Photography: Computational photography algorithms were used to enhance and manipulate images captured by cameras. These algorithms included techniques such as image enhancement, color correction, and image compression. Researchers developed and applied these algorithms to solve problems in areas such as photography, video editing, and computer vision.

Neural Networks: Neural networks, a type of artificial intelligence that mimics the behavior of the human brain, were extensively explored at ECCV 2022. Researchers developed and applied neural networks to solve problems in various domains, such as image recognition, speech recognition, and natural language processing.

Image Coding: Image coding algorithms were used to compress and encode images for storage and transmission. Researchers developed and applied these algorithms to solve problems in areas such as image compression, video compression, and data storage.

Image Reconstruction: Image reconstruction algorithms were used to recreate images from degraded or incomplete data. Researchers developed and applied these algorithms to solve problems in areas such as medical imaging, surveillance, and computer vision.

Object Recognition: Object recognition algorithms were used to identify and classify objects in images and videos. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve high accuracy and efficiency in object recognition tasks.

Motion Estimation: Motion estimation algorithms were used to estimate the motion of objects in images and videos. Researchers explored various techniques, including optical flow, particle filters, and deep learning, to achieve high accuracy and efficiency in motion estimation tasks.

The 17th European Conference on Computer Vision (ECCV) 2022 showcased a remarkable array of research and innovation in the field of computer vision. The high-quality papers presented at the conference covered a wide range of topics and demonstrated the significant progress made in this field. The proceedings of ECCV 2022 will serve as a valuable resource for researchers, practitioners, and students in the computer vision community, providing insights into the latest advancements and fostering future collaborations.

The 17th European Conference on Computer Vision (ECCV) 2022, held in Tel Aviv, Israel, from October 23 to 27, 2022, witnessed the presentation of a remarkable 1645 papers. These papers, meticulously reviewed and selected from a staggering 5804 submissions, showcased a diverse range of topics within the field of computer vision.

Computer 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 covered a broad spectrum of topics, including:

Computer Vision: This overarching field encompasses the development and application of algorithms for tasks such as image analysis, object detection, and scene understanding. Researchers explored innovative approaches to tackle challenging problems in computer vision, such as autonomous driving, medical imaging, and surveillance.

Machine Learning: Machine learning algorithms played a pivotal role in many of the presented papers. These algorithms are used to analyze and interpret data, enabling computers to make predictions and decisions based on patterns. Researchers explored various machine learning techniques, including deep learning, convolutional neural networks, and reinforcement learning, to address diverse computer vision tasks.

Deep Neural Networks: Deep neural networks, a powerful tool for analyzing and processing complex data, were extensively explored at ECCV 2022. Researchers developed and improved deep learning models for tasks such as image recognition, object detection, and facial recognition. These models demonstrated remarkable performance and opened up new avenues for solving complex vision problems.

Reinforcement Learning: Reinforcement learning, a technique that trains agents to make decisions in environments that are not fully observable, received significant attention at ECCV 2022. Researchers developed and applied reinforcement learning algorithms to solve problems in robotics, autonomous navigation, and game playing. These algorithms demonstrated the ability to learn from experience and improve performance over time.

Object Recognition: Object recognition algorithms were a central focus of many papers presented at ECCV 2022. Researchers developed and improved state-of-the-art models for identifying and classifying objects in images and videos. These models leveraged advanced techniques such as convolutional neural networks, deep learning, and transfer learning to achieve high accuracy and efficiency.

Image Classification: Image classification algorithms were used to categorize images based on their content, such as objects, landscapes, or animals. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve accurate and robust image classification.

Image Processing: Image processing algorithms were employed to enhance, modify, and analyze images. These algorithms included techniques such as image enhancement, image restoration, and image segmentation. Researchers developed and applied these algorithms to solve problems in various domains, such as medical imaging, surveillance, and computer vision.

Object Detection: Object detection algorithms were used to identify and locate objects in images and videos. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve high accuracy and efficiency in object detection tasks.

Semantic Segmentation: Semantic segmentation algorithms were used to divide an image into different regions based on their semantic meaning, such as objects, backgrounds, or textures. Researchers developed and applied these algorithms to solve problems in areas such as autonomous driving, medical imaging, and scene understanding.

Human Pose Estimation: Human pose estimation algorithms were used to determine the position and orientation of human bodies in images and videos. Researchers explored various techniques, including deep learning, convolutional neural networks, and transfer learning, to achieve accurate and robust pose estimation.

Three-D Reconstruction: Three-D reconstruction algorithms were used to create three-dimensional models of objects and scenes from two-dimensional images. Researchers developed and applied these algorithms to solve problems in areas such as archaeology, engineering, and computer vision.

Stereo Vision: Stereo vision algorithms were used to create depth perception and 3D reconstructions from multiple camera images. Researchers explored various techniques, including disparity estimation, feature matching, and stereo matching, to achieve high accuracy and efficiency in stereo vision tasks.

Computational Photography: Computational photography algorithms were used to enhance and manipulate images captured by cameras. These algorithms included techniques such as image enhancement, color correction, and image compression. Researchers developed and applied these algorithms to solve problems in areas such as photography, video editing, and computer vision.

Neural Networks: Neural networks, a type of artificial intelligence that mimics the behavior of the human brain, were extensively explored at ECCV 2022. Researchers developed and applied neural networks to solve problems in various domains, such as image recognition, speech recognition, and natural language processing.

Image Coding: Image coding algorithms were used to compress and encode images for storage and transmission. Researchers developed and applied these algorithms to solve problems in areas such as image compression, video compression, and data storage.

Image Reconstruction: Image reconstruction algorithms were used to recreate images from degraded or incomplete data. Researchers developed and applied these algorithms to solve problems in areas such as medical imaging, surveillance, and computer vision.

Object Recognition: Object recognition algorithms were used to identify and classify objects in images and videos. Researchers explored various techniques, including convolutional neural networks, deep learning, and transfer learning, to achieve high accuracy and efficiency in object recognition tasks.

Motion Estimation: Motion estimation algorithms were used to estimate the motion of objects in images and videos. Researchers explored various techniques, including optical flow, particle filters, and deep learning, to achieve high accuracy and efficiency in motion estimation tasks.

The 17th European Conference on Computer Vision (ECCV) 2022 showcased a remarkable array of research and innovation in the field of computer vision. The high-quality papers presented at the conference covered a wide range of topics and demonstrated the significant progress made in this field. The proceedings of ECCV 2022 will serve as a valuable resource for researchers, practitioners, and students in the computer vision community, providing insights into the latest advancements and fostering future collaborations.

Weight: 1223g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031200588
Edition number: 1st ed. 2022