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

The 39-volume set, consisting of the LNCS books 13661 until 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: 747 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), 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, 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, and motion estimation.

Introduction:
The 17th European Conference on Computer Vision (ECCV), held in Tel Aviv, Israel, from October 23-27, 2022, brought together researchers and practitioners from around the world to showcase the latest advancements in computer vision technology. The conference featured a wide range of presentations, including research papers, tutorials, and workshops, covering various aspects of computer vision, such as image processing, object detection, and machine learning.

Refereed Proceedings:
The 39-volume set of the refereed proceedings of ECCV 2022 comprises a comprehensive collection of papers presented at the conference. These papers were subjected to a rigorous review process, with experts in the field selecting the most innovative and significant contributions. The proceedings cover a wide range of topics in computer vision, including:

• Computer Vision: This encompasses the development and application of algorithms for image and video analysis, object detection, and scene understanding. The papers in this section explore new techniques for feature extraction, object tracking, and scene parsing, as well as their applications in autonomous vehicles, medical imaging, and surveillance systems.
  


• Machine Learning: Machine learning plays a crucial role in computer vision, with algorithms being developed to learn from data and improve performance over time. The papers in this section cover a variety of machine learning techniques, such as deep learning, convolutional neural networks, and reinforcement learning, and their applications in image classification, object detection, and facial recognition.
  
• Deep Neural Networks: Deep neural networks are a powerful tool for image and video processing, with their ability to learn complex patterns and features from data. The papers in this section explore the design and optimization of deep neural networks for various applications, such as image restoration, object detection, and facial recognition.
  
• Reinforcement Learning: Reinforcement learning is a technique for learning to make decisions in complex environments by receiving feedback from the environment. The papers in this section explore the use of reinforcement learning in autonomous vehicles, robotics, and game playing, as well as its applications in decision-making and optimization.
  
• Object Recognition: Object recognition is a fundamental task in computer vision, with the goal of identifying and categorizing objects in images and videos. The papers in this section explore new techniques for object recognition, such as deep learning-based methods and multi-view learning, and their applications in autonomous vehicles, surveillance systems, and augmented reality.
  
• Image Classification: Image classification is the task of assigning labels to images based on their content. The papers in this section explore new techniques for image classification, such as convolutional neural networks, transfer learning, and generative adversarial networks, and their applications in medical imaging, remote sensing, and image retrieval.
  
• Image Processing: Image processing is the manipulation and analysis of images to extract useful information. The papers in this section explore new techniques for image processing, such as image enhancement, denoising, and segmentation, and their applications in medical imaging, surveillance systems, and computer vision.
  
• Object Detection: Object detection is the task of identifying and locating objects in images and videos. The papers in this section explore new techniques for object detection, such as convolutional neural networks, deep learning-based methods, and multi-object tracking, and their applications in autonomous vehicles, surveillance systems, and augmented reality.
  
• Semantic Segmentation: Semantic segmentation is the task of dividing an image into different regions based on their semantic meaning. The papers in this section explore new techniques for semantic segmentation, such as convolutional neural networks, deep learning-based methods, and graph-based methods, and their applications in medical imaging, autonomous vehicles, and scene understanding.
  
• Human Pose Estimation: Human pose estimation is the task of determining the pose of a human body in an image or video. The papers in this section explore new techniques for human pose estimation, such as deep learning-based methods, multi-view learning, and motion tracking, and their applications in augmented reality, sports analytics, and human-computer interaction.
  
• 3D Reconstruction: 3D reconstruction is the process of generating a 3D model of a scene or object from multiple 2D images or videos. The papers in this section explore new techniques for 3D reconstruction, such as photogrammetry, depth estimation, and mesh generation, and their applications in virtual reality, archaeology, and engineering.
  
• Stereo Vision: Stereo vision is the task of determining the depth and distance of objects in a scene from two or more images. The papers in this section explore new techniques for stereo vision, such as disparity estimation, feature matching, and stereo matching, and their applications in autonomous vehicles, surveillance systems, and augmented reality.
  
• Computational Photography: Computational photography is the use of computer vision techniques to improve the quality and aesthetics of images and videos. The papers in this section explore new techniques for computational photography, such as image enhancement, color correction, and scene reconstruction, and their applications in photography, film production, and advertising.
  
• Neural Networks: Neural networks are a class of machine learning algorithms that are inspired by the structure and function of the human brain. The papers in this section explore new techniques for neural networks, such as convolutional neural networks, recurrent neural networks, and transformer-based networks, and their applications in image recognition, speech recognition, and natural language processing.
  
• Image Coding: Image coding is the process of compressing images and videos to reduce their size and improve their transmission speed. The papers in this section explore new techniques for image coding, such as lossless compression, lossy compression, and compression with adaptive quantization, and their applications in image transmission, storage, and retrieval.
  
• Image Reconstruction: Image reconstruction is the process of restoring an image or video from a set of corrupted or incomplete data. The papers in this section explore new techniques for image reconstruction, such as deep learning-based methods, iterative reconstruction, and sparse reconstruction, and their applications in medical imaging, remote sensing, and computer vision.
  

Weight: 1211g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031197680
Edition number: 1st ed. 2022