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

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: 757 pages
Publication date: 22 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 complex environments by receiving feedback in the form of rewards or penalties.

Object 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.

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

Image Processing: Image processing algorithms are used to enhance, modify, and analyze images, enabling tasks such as image restoration and image compression.

Object Detection: Object detection algorithms are used to identify and locate objects in images and videos, enabling applications such as security surveillance and autonomous navigation.

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

Human Pose Estimation: Human pose estimation algorithms are used to estimate the pose of a human body in images or videos, enabling applications such as virtual reality and human-computer interaction.

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

Stereo Vision: Stereo vision algorithms are used to create depth perception and 3D visualization from two-dimensional images or videos.

Computational Photography: Computational photography algorithms are used to enhance and manipulate images and videos using computer vision techniques.

Neural 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.

Image Coding: Image coding algorithms are used to reduce the size of digital images while maintaining their quality.

Image Reconstruction: Image reconstruction algorithms are used to recreate images from a set of noisy or incomplete measurements.

Object 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.

Motion Estimation: Motion estimation algorithms are used to estimate the motion of objects in images and videos, enabling applications such as video stabilization and motion tracking.

These proceedings represent the latest advancements in computer vision research and technology, and they will be valuable to 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, cover 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 delve into various aspects of computer vision, 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 explore innovative methods to enhance the accuracy, efficiency, and robustness of computer vision systems, enabling them to perform tasks in diverse environments and applications.

Machine 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.

Deep 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.

Reinforcement 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 systems, where agents need to learn to navigate and interact with their environment to achieve their goals.

Object Recognition: Object recognition algorithms are employed to identify and classify objects in images and videos. These algorithms utilize various techniques, such as convolutional neural networks, to extract features from the input data and classify objects based on their shape, texture, and other characteristics.

Image Classification: Image classification algorithms are used to categorize images based on their content. These algorithms employ convolutional neural networks to extract features from the images and classify them into different categories, such as flowers, animals, or buildings.

Image Processing: Image processing algorithms are employed to enhance, modify, and analyze images. These algorithms include techniques such as image enhancement, image compression, and image restoration. They are widely used in various applications, including medical imaging, surveillance, and multimedia.

Object Detection: Object detection algorithms are used to identify and locate objects in images and videos. These algorithms employ convolutional neural networks to extract features from the input data and classify objects based on their shape, size, and location.

Semantic Segmentation: Semantic segmentation algorithms are used to divide an image into different regions based on their semantic meaning. These algorithms employ convolutional neural networks to segment the image into different objects, such as objects, backgrounds, or faces.

Human Pose Estimation: Human pose estimation algorithms are employed to estimate the pose of a human body in images or videos. These algorithms utilize deep learning techniques to analyze the body's shape and position and estimate the pose of the body.

Three-Dimensional Reconstruction: Three-dimensional reconstruction algorithms are used to create three-dimensional models of objects and scenes from two-dimensional images or videos. These algorithms employ techniques such as depth perception, stereo vision, and photogrammetry.

Stereo Vision: Stereo vision algorithms are used to create depth perception and 3D visualization from two-dimensional images or videos. These algorithms utilize techniques such as disparity estimation, feature matching, and triangulation to reconstruct the 3D scene.

Computational Photography: Computational photography algorithms are used to enhance and manipulate images and videos using computer vision techniques. These algorithms include techniques such as image enhancement, color correction, and image stitching.

Neural 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.

Image Coding: Image coding algorithms are used to reduce the size of digital images while maintaining their quality. These algorithms employ techniques such as compression, quantization, and entropy coding.

Image Reconstruction: Image reconstruction algorithms are used to recreate images from a set of noisy or incomplete measurements. These algorithms employ techniques such as interpolation, denoising, and reconstruction techniques.

Object Recognition: Object recognition algorithms are used to identify and classify objects in images and videos. These algorithms employ convolutional neural networks to extract features from the input data and classify objects based on their shape, texture, and other characteristics.

Motion Estimation: Motion estimation algorithms are used to estimate the motion of objects in images and videos. These algorithms employ techniques such as optical flow, motion tracking, and particle filters.

The 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 computer vision community in addressing challenging problems and pushing the boundaries of what is possible with visual data.

The selection process for the papers at ECCV 2022 was rigorous, with each submission undergoing a thorough review by a panel of expert reviewers. The reviewers assessed the originality, significance, and technical quality of the papers, ensuring that only the most innovative and impactful research was accepted for presentation. The conference also provided opportunities for researchers to interact with each other, exchange ideas, and form collaborations, fostering a vibrant and dynamic research community.

The 1645 papers presented at ECCV 2022 cover a wide range of topics, reflecting the diverse nature of computer vision research. The papers address various applications, including autonomous vehicles, medical imaging, surveillance, and multimedia. The research presented at the conference spans different subfields of computer vision, including computer vision, machine learning, deep learning, and reinforcement learning.

The papers at ECCV 2022 showcase the latest advancements in computer vision technology, including deep neural networks, convolutional neural networks, and reinforcement learning algorithms. The researchers employ cutting-edge techniques and methodologies to solve complex problems and achieve state-of-the-art performance. The conference also provides a platform for researchers to explore new research directions and identify emerging trends in the field.

In conclusion, the 17th European Conference on Computer Vision (ECCV) 2022 was a resounding success, showcasing the latest advancements in computer vision research and technology. The high-quality papers presented at the conference highlight the significant contributions made by the computer vision community in addressing challenging problems and pushing the boundaries of what is possible with visual data. The conference also provided opportunities for researchers to interact with each other, exchange ideas, and form collaborations, fostering a vibrant and dynamic research community. The proceedings of the conference will be valuable to researchers, practitioners, and students in the field, serving as a reference for future research and development.

Weight: 1229g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031197864
Edition number: 1st ed. 2022