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

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: 23 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.

This comprehensive collection includes a total of 1645 papers, which were meticulously reviewed and selected from an overwhelming 5804 submissions. The papers cover a wide range of topics within the field of computer vision, including:

Computer Vision: This encompasses the study and development of algorithms and systems for understanding and interpreting visual data, such as images, videos, and 3D scenes. It involves techniques such as image processing, pattern recognition, object detection, and scene analysis.

Machine Learning: Machine learning is a branch of computer science that focuses on teaching computers to learn from data, without being explicitly programmed. It involves techniques such as supervised learning, unsupervised learning, and reinforcement learning, which are used to analyze and predict patterns in data.

Deep Neural Networks: Deep neural networks are a type of neural network that consists of multiple layers of interconnected neurons. They are capable of learning complex patterns and making highly accurate predictions. Deep neural networks are widely used in areas such as image recognition, speech recognition, and natural language processing.

Reinforcement Learning: Reinforcement learning is a type of machine learning that involves learning by interacting with an environment. The agent learns by receiving rewards or penalties for its actions and making decisions based on those rewards. Reinforcement learning is used in autonomous vehicles, game playing, and robotics.

Object Recognition: Object recognition is the process of identifying and categorizing objects in images or videos. It involves techniques such as feature extraction, classification, and tracking. Object recognition is widely used in areas such as security, surveillance, and autonomous systems.

Image Classification: Image classification is the process of assigning a label to an image based on its content. It involves techniques such as convolutional neural networks, decision trees, and support vector machines. Image classification is widely used in areas such as medical imaging, image retrieval, and content-based indexing.

Image Processing: Image processing is the process of manipulating and enhancing images to improve their quality, appearance, or functionality. It involves techniques such as image enhancement, restoration, segmentation, and feature extraction. Image processing is widely used in areas such as photography, video processing, and computer vision.

Object Detection: Object detection is the process of identifying and locating objects in images or videos. It involves techniques such as convolutional neural networks, bounding boxes, and region proposals. Object detection is widely used in areas such as security, surveillance, and autonomous systems.

Semantic Segmentation: Semantic segmentation is the process of dividing an image into different semantic regions, such as objects, backgrounds, and foreground. It involves techniques such as convolutional neural networks, deep learning, and graph-based methods. Semantic segmentation is widely used in areas such as medical imaging, autonomous vehicles, and scene understanding.

Human Pose Estimation: Human pose estimation is the process of determining the position and orientation of the body parts of a human in an image or video. It involves techniques such as deep learning, optical flow, and feature extraction. Human pose estimation is widely used in areas such as augmented reality, virtual reality, and human-computer interaction.

Three-Dimensional Reconstruction: Three-dimensional reconstruction is the process of creating a three-dimensional model of an object or scene from multiple two-dimensional images or videos. It involves techniques such as photogrammetry, point clouds, and mesh modeling. Three-dimensional reconstruction is widely used in areas such as archaeology, engineering, and entertainment.

Stereo Vision: Stereo vision is the process of obtaining depth information from two or more images of the same scene. It involves techniques such as disparity estimation, triangulation, and feature matching. Stereo vision is widely used in areas such as autonomous vehicles, robotics, and augmented reality.

Computational Photography: Computational photography is the use of computer algorithms to enhance or modify images or videos. It involves techniques such as image optimization, color correction, and image synthesis. Computational photography is widely used in areas such as photography, video editing, and gaming.

Neural Networks: Neural networks are a type of artificial intelligence that mimics the behavior of the human brain. They are composed of interconnected nodes that process information and make decisions. Neural networks are widely used in areas such as image recognition, speech recognition, and natural language processing.

Image Coding: Image coding is the process of compressing images or videos to reduce their size without losing quality. It involves techniques such as JPEG, PNG, and H.264/MPEG-4 AVC. Image coding is widely used in areas such as web browsing, video streaming, and data storage.

Image Reconstruction: Image reconstruction is the process of restoring an image or video that has been corrupted or degraded. It involves techniques such as interpolation, denoising, and reconstruction algorithms. Image reconstruction is widely used in areas such as medical imaging, satellite imaging, and astronomy.

Object Recognition: Object recognition is the process of identifying and categorizing objects in images or videos. It involves techniques such as feature extraction, classification, and tracking. Object recognition is widely used in areas such as security, surveillance, and autonomous systems.

Motion Estimation: Motion estimation is the process of determining the motion of objects in images or videos. It involves techniques such as optical flow, motion tracking, and feature extraction. Motion estimation is widely used in areas such as video editing, sports analysis, and autonomous vehicles.

In conclusion, the 39-volume set of the ECCV 2022 proceedings represents the state-of-the-art research in computer vision and its various subfields. The papers presented in this collection cover a wide range of topics, from fundamental research to practical applications, and demonstrate the significant advancements that have been made in this field over the past year. The contributions of the authors and reviewers to this proceedings are invaluable, and they will undoubtedly contribute to the continued growth and development of computer vision in the years to come.

Weight: 1229g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031198021
Edition number: 1st ed. 2022