Journal of Mathematical Imaging and Vision - Impact Factor

Journal of Mathematical Imaging and Vision - Springer | 2024 Impact Factor:1.5 | Cite Score:3.2 | Q2

Impact Factor and Journal Rank of Mathematical Imaging and Vision

About: The Journal of Mathematical Imaging and Vision (JMIV) is dedicated to publishing research that advances the understanding and application of mathematical and computational methods in imaging and vision sciences. It serves as a forum for both theoretical developments and practical applications in these fields.
Objectives
The primary objectives of JMIV include:
Theoretical Advancements: To publish original research that contributes significant theoretical insights into mathematical imaging and vision.
Methodological Innovations: To explore new computational methods and algorithms for image analysis, processing, and understanding.
Application in Vision Sciences: To bridge the gap between mathematical theories and their applications in computer vision, pattern recognition, and related disciplines.
Interdisciplinary Research: To foster collaboration between mathematicians, computer scientists, engineers, and vision scientists to address complex imaging problems.
Key Topics Covered
JMIV covers a wide range of topics related to mathematical imaging and vision, including:
Image Processing and Analysis: Techniques for image enhancement, segmentation, registration, and feature extraction.
Computer Vision: Methods for object detection, recognition, tracking, and scene understanding.
Mathematical Modeling: Mathematical frameworks for image representation, reconstruction, and visualization.
Machine Learning in Imaging: Applications of machine learning and deep learning in medical imaging, remote sensing, and other imaging modalities.
Biomedical Imaging: Techniques and applications in medical image analysis, including diagnostic imaging and image-guided therapy.
Remote Sensing and Geophysics: Imaging techniques and algorithms for earth observation, environmental monitoring, and geophysical imaging.
Pattern Recognition: Methods for recognizing patterns and structures in images, videos, and 3D data.
Impact and Significance
JMIV plays a crucial role in:
Advancing Research: By publishing cutting-edge research that pushes the boundaries of mathematical imaging and computer vision.
Technology Development: By fostering the development of new algorithms and methodologies that improve imaging techniques and applications.
Cross-Disciplinary Collaboration: By promoting collaboration between researchers from mathematics, computer science, engineering, and vision sciences.
Applications in Industry and Medicine: By facilitating the translation of research findings into practical applications in fields such as healthcare, robotics, autonomous systems, and more.
Education and Training: By providing a platform for dissemination of knowledge and education in the field of mathematical imaging and vision.

Journal Home: Journal Homepage

Editor-in-Chief: Jean-Michel Morel

Scope: Journal of Mathematical Imaging and Vision publishes research articles that integrate mathematical and computational methodologies for analyzing, processing, and interpreting images and visual data. The journal covers a broad spectrum of topics within mathematical imaging and vision, including:
Image Processing: Techniques and algorithms for image enhancement, restoration, segmentation, and compression.
Computer Vision: Methods for object detection, recognition, tracking, and 3D reconstruction from images and video sequences.
Mathematical Modeling: Mathematical models and frameworks for representing and analyzing visual data.
Image Analysis: Statistical and geometric approaches to analyze patterns and structures in images.
Pattern Recognition: Machine learning techniques for pattern recognition and classification in visual data.
Medical Imaging: Applications of imaging techniques in medical diagnostics and healthcare.
Remote Sensing: Image processing and analysis for remote sensing applications, including environmental monitoring and geospatial analysis.
Biometric Recognition: Computational methods for biometric identification and authentication using visual data.