Journal of Imaging

Top Research Topics at Journal of Imaging?

The concepts of Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Convolutional neural network are tackled in the journal. Journal of Imaging dives deep in exploring the relationship between the study of Artificial intelligence and Machine learning. Pattern recognition research presented in the journal encompasses a variety of subjects, including Local binary patterns and Feature (computer vision).

The journal held discussions to help close the divide between two different fields of study: Computer vision and Process (computing). Journal of Imaging facilitates discussions on Deep learning that incorporate concepts from other fields like Artificial neural network and Transfer of learning. Specifically, studies on Image segmentation are prevalent in the Segmentation works discussed.

• Artificial intelligence (52.52%)
• Pattern recognition (22.41%)
• Computer vision (20.03%)

What are the most cited papers published in the journal?

• An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos (191 citations)
• Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. (87 citations)
• Object Recognition in Aerial Images Using Convolutional Neural Networks (84 citations)

Research areas of the most cited articles at Journal of Imaging:

The published papers primarily tackle Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Computer vision. Artificial intelligence research in the journal papers connects with the study of Field (computer science). While Deep learning is the focus of the published papers, it also provides insights into the studies of Domain (software engineering), Variety (cybernetics), Information retrieval and Data science.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Quantum mechanics
• Optics

The previous edition focused in particular on these issues:

The discussions in Journal of Imaging mainly cover the fields of Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Convolutional neural network. Artificial intelligence and Machine learning are closely related fields of research discussed in it. While work presented in it provided substantial information on Pattern recognition, it also covered topics in Image processing, Medical imaging, Image quality and Benchmark (computing).

The work on Deep learning tackled in Journal of Imaging brings together disciplines like Transfer of learning and Face (geometry). Journal of Imaging features works in Computer vision, more specifically Ground truth and Object detection, and explores their relation to disciplines like Process (computing). It addresses concerns in Convolutional neural network which are intertwined with other disciplines, such as Contextual image classification and Field (computer science).

The most cited articles from the last journal are:

• An Update of the Possible Applications of Magnetic Resonance Imaging (MRI) in Dentistry: A Literature Review. (9 citations)
• Skin Lesion Segmentation Using Deep Learning with Auxiliary Task (6 citations)
• A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics (4 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Journal of Imaging (based on the number of publications) are:

• Constantino Carlos Reyes-Aldasoro (7 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Kees Joost Batenburg (7 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Zion Tsz Ho Tse (6 papers) absent at the last edition,
• Cosimo Distante (5 papers) published 2 papers at the last edition the same number as at the previous edition,
• Javaan Chahl (5 papers) published 4 papers at the last edition, 3 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Imaging (based on the number of publications) are:

• National Research Council (12 papers) published 7 papers at the last edition, 3 more than at the previous edition,
• Sapienza University of Rome (8 papers) published 6 papers at the last edition, 5 more than at the previous edition,
• University of Catania (7 papers) published 4 papers at the last edition, 2 more than at the previous edition,
• Norwegian University of Science and Technology (7 papers) published 3 papers at the last edition, 1 more than at the previous edition,
• Technische Universität München (6 papers) published 3 papers at the last edition the same number as at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 20.18% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.82% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.62% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.24% of all publications and 56.32% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Practical Applications and Career Opportunities in Imaging Research

As per the Google Search Quality Guidelines, adding a section that covers practical applications and discusses potential career paths can enhance the depth and increase the website's overall value. This section can be particularly useful for individuals interested in pursuing a career in this field, such as becoming a high school history teacher. In the field of imaging research, classical studies span across Artificial Intelligence, Computer Vision, Pattern Recognition, and Convolutional Neural Networks, all of which offer a plethora of potential applications. From developing today’s most advanced facial recognition systems and self-driving vehicles to designing state-of-the-art medical imaging algorithms, imaging research is at the heart of these advancements. The burgeoning technologies of AI and Deep Learning further supercharge this field’s evolving applications, pushing the frontiers of what is possible in both the business and academic sectors.

Given such a wide-reaching importance and application, the job market in imaging research is also diverse, ranging from advanced roles in research and academia to teaching positions in high schools and universities. For those looking to indulge their passion for history combined with technology, becoming a high school history teacher who also provides insight into imaging technology could be a rewarding career choice. An understanding of how these tools can animate and enrich historical studies could be critical for the next generation learning history. Interested individuals can explore details about this particular career, such as required qualifications, average earnings, and day-to-day duties from our detailed guide on how much does a high school history teacher make in Arizona.