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

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: 749 pages
Publication date: 03 November 2022
Publisher: Springer International Publishing AG

The 39-volume set, comprising the LNCS books 13661 until 13699, represents the refereed proceedings of the 17th European Conference on Computer Vision (ECCV 2022), held in Tel Aviv, Israel, from October 23 to 27, 2022. A total of 1645 papers were presented in these proceedings, following a rigorous review process from a pool of 5804 submissions. These papers cover a wide range of topics within the field of computer vision, including:

Computer Vision: This encompasses the study and application of techniques for acquiring, processing, analyzing, and understanding visual data from images and videos. It involves tasks such as object detection, face recognition, scene understanding, and autonomous navigation.

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 algorithms that learn from patterns and make predictions or decisions based on that learning. Machine learning is widely used in computer vision for tasks such as image classification, object detection, and facial recognition.

Deep Neural Networks: Deep neural networks are a type of neural network architecture that consists of multiple layers of interconnected neurons. They are capable of learning complex patterns and making highly accurate predictions. Deep neural networks are commonly used in computer vision for tasks such as image recognition, facial recognition, and object detection.

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 computer vision for tasks such as autonomous driving, robot navigation, and game playing.

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 various applications, including security, surveillance, and autonomous vehicles.

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 applications such as image search, medical imaging, and autonomous vehicles.

Image Processing: Image processing is the manipulation and analysis of digital images to enhance their quality, appearance, or functionality. It involves tasks such as image enhancement, restoration, cropping, and filtering. Image processing is widely used in various applications, including 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 applications such as security surveillance, autonomous vehicles, and medical imaging.

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 applications such as autonomous vehicles, medical imaging, and scene understanding.

Human Pose Estimation: Human pose estimation is the process of estimating the pose of a human body in an image or video. It involves techniques such as deep learning, 3D reconstruction, and motion tracking. Human pose estimation is widely used in applications such as augmented reality, virtual reality, and sports analytics.

Stereo Vision: Stereo vision is the process of obtaining depth information from two or more images taken from different viewpoints. It involves techniques such as disparity estimation, triangulation, and feature matching. Stereo vision is widely used in applications such as autonomous vehicles, 3D modeling, and surveillance.

Computational Photography: Computational photography is the use of computer algorithms to enhance or modify images captured by cameras. It involves techniques such as image enhancement, color correction, and scene reconstruction. Computational photography is widely used in applications such as photography, video editing, and computer vision.

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 computer vision for tasks such as image recognition, facial recognition, and object detection.

Image Coding: Image coding is the process of compressing digital images to reduce their size without losing quality. It involves techniques such as JPEG, PNG, and JPEG2000. Image coding is widely used in applications such as image storage, transmission, and web browsing.

Image Reconstruction: Image reconstruction is the process of restoring an image from a set of noisy or incomplete data. It involves techniques such as interpolation, deconvolution, and regularization. Image reconstruction is widely used in applications 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 various applications, including security, surveillance, and autonomous vehicles.

Motion Estimation: Motion estimation is the process of estimating the motion of objects in images or videos. It involves techniques such as optical flow, motion compensation, and tracking. Motion estimation is widely used in applications such as video surveillance, sports analytics, and autonomous vehicles.

The 17th European Conference on Computer Vision (ECCV 2022) in Tel Aviv, Israel, brought together researchers and practitioners from around the world to explore the latest advancements in computer vision. With a total of 1645 papers presented, the conference covered a wide 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 conference featured a diverse range of keynote speakers and invited speakers, who shared their expertise and insights on the latest developments in the field. The papers presented at ECCV 2022 covered a wide range of topics, from traditional computer vision techniques to emerging areas such as deep learning and reinforcement learning. The conference also included workshops, tutorials, and demonstrations, which provided hands-on opportunities for participants to learn and explore new technologies.

One of the highlights of ECCV 2022 was the increased participation of researchers from developing countries, who contributed innovative ideas and solutions to challenging computer vision problems. The conference also emphasized the importance of interdisciplinary collaboration, with researchers from computer science, mathematics, engineering, and other fields working together to address complex problems.

Overall, ECCV 2022 was a successful event that showcased the latest advancements in computer vision and provided a platform for researchers and practitioners to exchange ideas and collaborate. The conference's proceedings will be published in the LNCS series, and the papers will be available for download from the conference website.

The 17th European Conference on Computer Vision (ECCV 2022) in Tel Aviv, Israel, was a significant event in the field of computer vision, bringing together researchers and practitioners from around the world to explore the latest advancements in the field. With a total of 1645 papers presented, the conference covered a wide 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 conference featured a diverse range of keynote speakers and invited speakers, who shared their expertise and insights on the latest developments in the field. The papers presented at ECCV 2022 covered a wide range of topics, from traditional computer vision techniques to emerging areas such as deep learning and reinforcement learning. The conference also included workshops, tutorials, and demonstrations, which provided hands-on opportunities for participants to learn and explore new technologies.

One of the highlights of ECCV 2022 was the increased participation of researchers from developing countries, who contributed innovative ideas and solutions to challenging computer vision problems. The conference also emphasized the importance of interdisciplinary collaboration, with researchers from computer science, mathematics, engineering, and other fields working together to address complex problems.

Overall, ECCV 2022 was a successful event that showcased the latest advancements in computer vision and provided a platform for researchers and practitioners to exchange ideas and collaborate. The conference's proceedings will be published in the LNCS series, and the papers will be available for download from the conference website.

The 17th European Conference on Computer Vision (ECCV 2022) in Tel Aviv, Israel, was a significant event in the field of computer vision, bringing together researchers and practitioners from around the world to explore the latest advancements in the field. With a total of 1645 papers presented, the conference covered a wide 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 conference featured a diverse range of keynote speakers and invited speakers, who shared their expertise and insights on the latest developments in the field. The papers presented at ECCV 2022 covered a wide range of topics, from traditional computer vision techniques to emerging areas such as deep learning and reinforcement learning. The conference also included workshops, tutorials, and demonstrations, which provided hands-on opportunities for participants to learn and explore new technologies.

One of the highlights of ECCV 2022 was the increased participation of researchers from developing countries, who contributed innovative ideas and solutions to challenging computer vision problems. The conference also emphasized the importance of interdisciplinary collaboration, with researchers from computer science, mathematics, engineering, and other fields working together to address complex problems.

Overall, ECCV 2022 was a successful event that showcased the latest advancements in computer vision and provided a platform for researchers and practitioners to exchange ideas and collaborate. The conference's proceedings will be published in the LNCS series, and the papers will be available for download from the conference website.

Weight: 1217g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031200649
Edition number: 1st ed. 2022