International Journal of Computer Vision - Impact Factor

International Journal of Computer Vision - Springer | 2024 Impact Factor:9.3 | Cite Score:25.9 | Q1

Impact Factor and Journal Rank of International Journal of Computer Vision

About:The International Journal of Computer Vision (IJCV) is a premier scholarly journal published by Springer, dedicated to the publication of high-quality research in the field of computer vision. The journal covers a wide range of topics related to the theory, methodology, and applications of computer vision, serving as a leading platform for disseminating significant advancements and novel contributions in this rapidly evolving field.
Content: The journal publishes original research articles that present novel theoretical insights, innovative methodologies, and practical applications in computer vision. It also includes review papers, surveys, and occasionally, special issues focused on emerging topics and significant advancements in the field.
Audience: IJCV is aimed at a diverse audience, including researchers, academics, students, and professionals in computer vision, artificial intelligence, machine learning, robotics, and related areas. The journals content is designed to cater to both specialists conducting cutting-edge research and practitioners applying computer vision techniques in various domains.
Interdisciplinary Approach: The journal emphasizes an interdisciplinary approach, integrating insights from computer science, engineering, mathematics, cognitive science, and other related fields. This fosters comprehensive research that addresses complex challenges in computer vision and contributes to the development of robust and versatile vision systems.
High Standards and Impact: IJCV maintains rigorous standards of quality and rigor through a thorough peer-review process, ensuring the publication of scientifically sound, innovative, and impactful research. The journal is widely recognized for its high impact and influence, with articles frequently cited by researchers and practitioners across the globe.
Global Reach: As an international journal, IJCV attracts contributions from leading researchers and institutions worldwide, facilitating the exchange of ideas and advancements in computer vision across different regions and scientific communities. This global perspective enhances the relevance and applicability of the research published.
Significance: It is a leading journal dedicated to advancing research in computer vision. Through its high standards of quality, interdisciplinary approach, and global reach, IJCV serves as a valuable resource for researchers, practitioners, and professionals seeking to contribute to and stay informed about the latest developments in the field.

Journal Home: Journal Homepage

Editor-in-Chief: Yasuyuki Matsushita

Scope:The International Journal of Computer Vision (IJCV) is a leading scholarly journal that focuses on the field of computer vision, which involves the development of algorithms and systems for interpreting, understanding, and analyzing visual data from the world. The scope of the journal is broad and covers a wide range of topics related to computer vision, including both theoretical and applied research.
Here are some key areas covered by the International Journal of Computer Vision:
Image and Video Analysis: Research on techniques for analyzing static images and video sequences. This includes object detection, recognition, and tracking, motion analysis, and event detection.
3D Vision and Reconstruction: Methods for understanding and reconstructing three-dimensional scenes from images or video. This includes stereo vision, structure-from-motion, depth estimation, and 3D shape modeling.
Feature Extraction and Representation: Techniques for extracting and representing features from visual data, such as edges, corners, textures, and keypoints. This also includes research on descriptors and invariant features.
Segmentation and Grouping: Algorithms for partitioning images into meaningful regions or segments. This includes techniques for image segmentation, superpixel generation, and perceptual grouping.
Machine Learning in Vision: Application of machine learning techniques to computer vision problems. This includes deep learning, convolutional neural networks (CNNs), reinforcement learning, and other AI approaches for visual data analysis.
Pattern Recognition: Methods for recognizing patterns and objects in visual data. This includes face recognition, handwriting recognition, biometrics, and classification tasks.
Image Synthesis and Enhancement: Techniques for generating and enhancing visual content. This includes image inpainting, super-resolution, style transfer, and computational photography.
Vision for Graphics: Research at the intersection of computer vision and computer graphics. This includes image-based rendering, texture synthesis, and augmented reality.
Human-Computer Interaction: Development of vision-based interfaces and systems for interacting with computers and devices. This includes gesture recognition, eye tracking, and user behavior analysis.
Visual Perception and Cognition: Studies on computational models of human visual perception and cognition. This includes research on visual attention, saliency detection, and visual scene understanding.