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

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: 745 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 analyze patterns and make predictions based on the data. 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 autonomous driving.

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 navigation, robotic manipulation, 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 surveillance, autonomous vehicles, and medical imaging.

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, facial recognition, and object detection.

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, compression, and segmentation. Image processing is widely used in various applications, including medical imaging, surveillance, and computer graphics.

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 autonomous vehicles, surveillance, 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 driving, medical imaging, 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 detection. Human pose estimation is widely used in applications such as augmented reality, virtual reality, and sports analytics.

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 cloud processing, and mesh modeling. Three-dimensional reconstruction is widely used in applications 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 applications such as autonomous vehicles, surveillance, 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 enhancement, color correction, and scene reconstruction. Computational photography is widely used in applications 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 computer vision for tasks such as image recognition, facial recognition, and autonomous driving.

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 web browsing, image storage, and video streaming.

Image Reconstruction: Image reconstruction is the process of restoring an image from a set of corrupted or incomplete data. It involves techniques such as interpolation, denoising, and reconstruction algorithms. 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 surveillance, autonomous vehicles, and medical imaging.

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, invited talks, and technical sessions, where researchers presented their latest findings and discussed the future directions of computer vision research. Some of the notable highlights of the conference included:

Keynote Speaker: Prof. Yann LeCun, Director of the NYU Center for Data Science, gave a keynote speech on "Advances in Neural Networks and Machine Learning." He discussed the latest developments in deep learning, including convolutional neural networks, recurrent neural networks, and transformer-based models, and their applications in various fields, including computer vision.

Invited Talk: Prof. Jitendra Malik, Professor of Computer Science at the University of California, Berkeley, gave an invited talk on "Object Detection and Segmentation with Deep Convolutional Networks." He discussed the state-of-the-art techniques for object detection and segmentation, including deep convolutional neural networks, cascaded convolutional neural networks, and multi-scale approaches.

Technical Session: A technical session on "Image Recognition and Retrieval" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for image recognition, including convolutional neural networks, transfer learning, and deep learning-based methods.

Technical Session: A technical session on "3D Reconstruction and Visualization" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for 3D reconstruction, including photogrammetry, point cloud processing, and mesh modeling.

Technical Session: A technical session on "Human Pose Estimation" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for human pose estimation, including deep learning-based methods, optical flow, and feature detection.

Technical Session: A technical session on "Stereo Vision" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for stereo vision, including disparity estimation, triangulation, and feature matching.

Technical Session: A technical session on "Computational Photography" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for computational photography, including image enhancement, color correction, and scene reconstruction.

Technical Session: A technical session on "Neural Networks" featured presentations by researchers from various institutions, including the University of Oxford, the University of California, Berkeley, and the University of Toronto. The session discussed the latest techniques for neural networks, including convolutional neural networks, recurrent neural networks, and transformer-based models.

The 17th European Conference on Computer Vision (ECCV 2022) in Tel Aviv, Israel, was a significant event in the field of computer vision, providing a platform for researchers and practitioners to exchange ideas, share their latest findings, and collaborate on future research projects. The conference showcased the latest advancements in computer vision and laid the foundation for future breakthroughs in this rapidly evolving field.

Weight: 1211g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031200823
Edition number: 1st ed. 2022