| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Electronics and Electrical Engineering | 449 | 12 | 14 | 5 |
| Computer Science | 590 | 111 | 135 | 9 |
The primary areas of discussion in Journal of Electronic Imaging are Artificial intelligence, Computer vision, Image processing, Pattern recognition and Algorithm. Artificial intelligence studies presented in Journal of Electronic Imaging focus on topics such as Image segmentation, Feature extraction, Image quality, Visualization and RGB color model. The featured Image segmentation research is covered under the field of Segmentation.
The studies on RGB color model discussed can also contribute to research in the domains of Color space and Color image. Pixel, Image compression, Image restoration, Image resolution and Motion estimation studies are all carried out as a component of the study in Computer vision presented. Journal of Electronic Imaging facilitates discussions on Image compression that incorporate concepts from other fields like Data compression and Quantization (image processing).
Research in Image processing tackled falls within the umbrella of Image (mathematics). Topics in Pattern recognition were tackled in line with various other fields like Contextual image classification, Artificial neural network, Data modeling and Feature (computer vision).
The most cited articles cover a variety of subjects, including Artificial intelligence, Computer vision, Image processing, Pattern recognition and Algorithm. The most cited papers link adjacent topics like Computer vision with Computer graphics (images). The journal papers address concerns in Image processing which are intertwined with other disciplines, such as Image resolution and Human visual system model.
The journal mostly deals with topics like Artificial intelligence, Pattern recognition, Image processing, Feature extraction and Computer vision. Journal of Electronic Imaging tackles issues in Artificial intelligence, particularly in the topics of Feature (computer vision), Convolutional neural network, Deep learning, Image quality and Artificial neural network. Issues in Pattern recognition were discussed, taking into consideration concepts from other disciplines like Data modeling, Network architecture and Contextual image classification.
The work on Image processing tackled in it brings together disciplines like Pixel, Algorithm and Image fusion. Topics in Feature extraction explored in Journal of Electronic Imaging were investigated in conjunction with research in Image segmentation, Field (computer science), Visualization, Discriminative model and Pyramid (image processing). The presentations discussing Computer vision offer insights in topics such as Image (mathematics) and RGB color model.
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.
The top authors publishing in Journal of Electronic Imaging (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Electronic Imaging (based on the number of publications) are:
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.
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, 28.83% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 3.59% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.79% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.82% of all publications and 71.79% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Understanding electronic imaging concepts can also be beneficial in various sectors, including education. For instance, a history teacher can enhance the delivery of their lessons by utilizing tools developed from the study of artificial intelligence, computer vision, and image processing. In the context of Oklahoma, specifically, becoming a history teacher requires a specific set of qualifications. Familiarity with electronic imaging can serve as a unique skillset, potentially giving candidates a competitive edge. Insights gained from the research published in the Journal of Electronic Imaging can help aspiring educators realize the potential of technology in shaping the future of education. To learn more about the requirements for becoming a history teacher in Oklahoma, which now includes technical skills beyond just subject matter expertise, you can refer to history teacher requirements in Oklahoma. This resource provides a comprehensive guide on the academic qualifications, certification process and key skills required to excel in the field of teaching, and highlights how advancing technologies, such as electronic imaging, continue to redefine the scope and approach of traditional roles across all industries.
Sawinder Kaur;Parteek Kumar;Ponnurangam Kumaraguru
(2020)Hiya Roy;Subhajit Chaudhury;Toshihiko Yamasaki;Tatsuaki Hashimoto
(2021)Alice Plutino;Barbara Rita Barricelli;Elena Casiraghi;Alessandro Rizzi
(2021)Arpan Basu;Riktim Mondal;Showmik Bhowmik;Ram Sarkar
(2020)Muhammad Ali Farooq;Hossein Javidnia;Hossein Javidnia;Peter Corcoran
(2020)For students exploring Computer Science, related fields such as engineering, physics, and data science offer complementary skills and diverse career opportunities. Considering an online degree can provide flexibility and affordability, especially when balancing studies with work or other commitments.
Mechanical engineering shares foundational problem-solving skills with computer science, and many students look for the cheapest online mechanical engineering degree programs to enter this dynamic field without heavy financial burden.
Similarly, studying the physical principles behind technology can be enriched by pursuing the cheapest online physics degree. This path creates a strong analytical foundation valuable in scientific computing and hardware development.
Data science, a rapidly growing area related to computer science, involves managing and interpreting large datasets. Following a structured data science learning path can help learners build expertise in analytics, machine learning, and artificial intelligence.
For those interested in hardware and systems design, engineering disciplines like electrical engineering are an excellent match. Discovering the best online electrical engineering programs USA provides access to top-tier resources and hands-on experience in circuit design, digital systems, and embedded programming.
Exploring these related online degrees can broaden your technical knowledge and open multiple pathways in the tech-driven job market.
