JOURNAL OF MATHEMATICAL IMAGING AND VISION

Scope & Guideline

Advancing the Frontiers of Mathematical Imaging and Vision

Introduction

Welcome to your portal for understanding JOURNAL OF MATHEMATICAL IMAGING AND VISION, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN0924-9907
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1992 to 2024
AbbreviationJ MATH IMAGING VIS / J. Math. Imaging Vis.
Frequency9 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The Journal of Mathematical Imaging and Vision focuses on the intersection of mathematics, computer science, and imaging technology. It aims to foster research that advances the mathematical methodologies and computational techniques used in image processing, analysis, and visualization.
  1. Mathematical and Computational Methods:
    The journal emphasizes the development and application of mathematical models and computational algorithms for image processing tasks, such as segmentation, denoising, and reconstruction.
  2. Geometric Analysis in Imaging:
    A significant focus is placed on geometric approaches to image understanding, including shape analysis, topology preservation, and morphological operations.
  3. Statistical and Probabilistic Methods:
    Research often explores statistical models and probabilistic frameworks for image interpretation, including uncertainty quantification and machine learning techniques.
  4. Applications Across Domains:
    The journal covers a wide range of applications, from medical imaging to remote sensing and computer vision, illustrating the interdisciplinary nature of the field.
  5. Emerging Technologies in Imaging:
    It also highlights innovative approaches, including deep learning and neural networks, to enhance traditional image processing techniques.
The Journal of Mathematical Imaging and Vision is currently experiencing a surge in specific themes that are reflective of the latest advancements and interests in the field. These emerging scopes highlight the journal's responsiveness to new challenges and technologies in imaging.
  1. Deep Learning for Image Processing:
    There is a significant trend towards the application of deep learning techniques, including convolutional neural networks, for various image processing tasks, enhancing both performance and efficiency.
  2. Uncertainty Quantification:
    Recent publications increasingly focus on uncertainty quantification in imaging, addressing the need for robust methods that can handle noise and variability in data.
  3. Hybrid Models and Algorithm Integration:
    A growing interest in hybrid models that combine different mathematical and computational techniques is evident, indicating a trend towards more comprehensive solutions for complex imaging problems.
  4. Geometric Deep Learning:
    Emerging research in geometric deep learning is gaining traction, integrating concepts from geometry into neural network architectures to improve image understanding.
  5. Real-Time Processing Techniques:
    With advancements in computational power, there is a rising focus on real-time image processing techniques, driven by applications in areas such as autonomous systems and interactive media.

Declining or Waning

Over time, the Journal of Mathematical Imaging and Vision has seen a gradual decline in certain research themes. These waning scopes reflect shifting interests and advancements in technology that have influenced the journal's focus.
  1. Traditional Image Processing Techniques:
    There has been a noticeable decrease in publications focusing on classical image processing methods, such as basic filtering and simple edge detection, as more advanced algorithms gain popularity.
  2. Basic Morphological Operations:
    The foundational morphological techniques, while still relevant, are appearing less frequently as researchers explore more complex and computationally sophisticated methods.
  3. Single-Use Models in Imaging:
    Research that relies on single-use or narrowly defined models is declining, with a shift towards more integrated and versatile approaches that can adapt to various imaging scenarios.
  4. Manual Feature Extraction:
    As automated and machine learning-based techniques become more prevalent, the focus on manual feature extraction methods is diminishing.

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