Computer Vision and Image Understanding - Impact Factor

Computer Vision and Image Understanding - Elsevier | 2024 Impact Factor:3.5 | Cite Score:7.1 | Q1

Computer Vision and Image Understanding Journal

Impact Factor and Journal Rank of Computer Vision and Image Understanding

About: The Computer Vision and Image Understanding Journal is a peer-reviewed publication by Elsevier. It focuses on research in the field of computer vision and image understanding, covering a wide range of topics such as image processing, pattern recognition, machine learning, and artificial intelligence. The journal serves as a platform for scholars, researchers, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of computer vision technologies.
Objective: The primary objective of the Computer Vision and Image Understanding Journal is to promote research and innovation in computer vision and image understanding. The journal aims to explore theories, methodologies, algorithms, and applications that enhance the understanding, interpretation, and analysis of visual data. By publishing high-quality research, the journal seeks to address the challenges and opportunities in developing computer vision systems that can perceive, comprehend, and interpret visual information.
Interdisciplinary Focus: The Computer Vision and Image Understanding Journal adopts an interdisciplinary approach, welcoming contributions from various fields related to computer vision and image processing, including but not limited to, Image Processing, Pattern Recognition, Machine Learning, Artificial Intelligence, Computer Graphics, Robotics, Medical Imaging, Remote Sensing, Augmented Reality, Human-Computer Interaction. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced computer vision technologies that address real-world challenges in diverse applications.
Global Reach and Impact: With a broad international readership and authorship, the Computer Vision and Image Understanding Journal has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in computer vision worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as autonomous vehicles, surveillance systems, medical diagnosis, industrial automation, and multimedia content analysis.
High Standards and Rigorous Review: Maintaining high academic standards, the Computer Vision and Image Understanding Journal conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: The Computer Vision and Image Understanding Journal plays a significant role in advancing research and practice in the field of computer vision. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in computer vision technologies and applications. It serves as an essential resource for researchers, practitioners, educators, and students interested in understanding and leveraging visual information to solve complex problems and enhance human-computer interaction.

Journal Home: Journal Homepage

Editor-in-Chief: Niculae Sebe

Scope: Computer Vision and Image Understanding, published by Elsevier, is a renowned peer-reviewed journal that focuses on advancing research in computer vision, image processing, and pattern recognition. It serves as a significant platform for researchers, practitioners, and scholars to share their latest findings, methodologies, and practical insights in the field of visual understanding. Here is an overview of its key focus areas and scope:
1. Image Processing Techniques:
Research on fundamental image processing algorithms and techniques for enhancing, analyzing, and interpreting digital images.
Advancements in image enhancement, restoration, segmentation, feature extraction, and morphological processing.
2. Computer Vision Algorithms:
Exploration of computer vision algorithms and methods for understanding and interpreting the content of images and videos.
Research on object detection, recognition, tracking, pose estimation, and scene understanding in complex visual scenes.
3. Pattern Recognition and Machine Learning:
Advancements in pattern recognition algorithms, machine learning techniques, and deep learning models for visual pattern analysis and classification.
Research on supervised, unsupervised, and semi-supervised learning approaches for recognizing objects, faces, gestures, and activities in images and videos.
4. 3D Computer Vision and Reconstruction:
Exploration of techniques for 3D reconstruction, stereo vision, depth estimation, and structure-from-motion from 2D images.
Research on point cloud processing, surface reconstruction, 3D object recognition, and scene understanding in three-dimensional space.
5. Image Understanding and Interpretation:
Advancements in image understanding techniques for inferring semantic meaning, context, and relationships from visual data.
Research on image captioning, visual question answering, image synthesis, and generative models for generating realistic visual content.
6. Biomedical Image Analysis:
Exploration of image processing and analysis techniques for medical imaging applications, including MRI, CT, PET, and microscopy images.
Research on computer-aided diagnosis (CAD), medical image segmentation, registration, and quantitative analysis for disease diagnosis and treatment planning.
7. Video Analysis and Understanding:
Advancements in video processing, action recognition, event detection, and temporal analysis of video sequences.
Research on video summarization, activity recognition, anomaly detection, and surveillance systems for understanding dynamic visual scenes.
8. Deep Learning for Computer Vision:
Exploration of deep learning architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for visual recognition tasks.
Research on transfer learning, domain adaptation, and network optimization techniques for improving the performance of deep learning models in computer vision.
9. Applications of Computer Vision:
Advancements in real-world applications of computer vision technology, including autonomous vehicles, robotics, augmented reality, virtual reality, and smart surveillance.
Research on visual content analysis for multimedia retrieval, image-based localization, object detection in satellite imagery, and industrial automation.