| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 248 | 174 | 200 | 22 |
| Electronics and Electrical Engineering | 364 | 21 | 27 | 8 |
The primary areas of discussion in the journal are Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Feature extraction. Iet Image Processing concentrated on Artificial intelligence research, specifically Image segmentation, Contextual image classification, Segmentation, Image (mathematics) and Pixel. Most of the works presented in it deals with Image segmentation but it intersects with the subject of Cluster analysis.
Pattern recognition research featured in Iet Image Processing incorporates concerns from various other topics such as Object detection and Feature (computer vision). Image processing, Edge detection, Image restoration, Image quality and Image resolution are all topics related to Computer vision research discussed. While work presented in the journal provided substantial information on Algorithm, it also covered topics in Coding (social sciences) and Wavelet transform.
While Feature extraction is the focus of it, it also provided insights into the studies of Facial recognition system, Image fusion, Robustness (computer science) and Feature vector. Topics in Convolutional neural network were tackled in line with various other fields like Artificial neural network and Deep learning. It dives deep in exploring the relationship between the study of Data compression and Image compression.
The journal publications mainly tackle studies in Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing. In addition to Pattern recognition research, the most cited articles aim to explore topics under Noise reduction and Image retrieval. While Image processing is the focus of the journal publications, it also provides insights into the studies of Image quality and Cryptography.
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 Iet Image Processing (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 Iet Image Processing (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, 5.97% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.99% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.76% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.22% of all publications and 61.03% 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.
A considerable percentage of research accomplished in the Iet Image Processing journal make valuable contributions to different areas of study, including the field of art education. For instance, advancements in Artificial Intelligence and computer vision have opened new horizons in teaching methodologies, particularly for subjects like arts. These advancements are significantly useful in fields like high school art education where technology can be used to improve student engagement and outcomes.
One prime example of this is the use of image processing and AI for better representation and analysis of artworks. With the help of feature extraction and pattern recognition, intricate details of art pieces can be brought to light, enriching the learning experience for students. This is especially useful in teaching art history or art appreciation where detailed analyses of artworks are necessary.
In the state of Rhode Island, for example, high school art teachers can leverage these technologies to offer a more comprehensive and interactive approach to art education. To understand more about these opportunities, you can read our detailed guide on how to become a high school art teacher in Rhode Island.
Overall, it is evident that the cutting-edge technologies discussed in the Iet Image Processing journal have the potential to revolutionize art education, thereby moulding a new generation of artists who are well-versed with both traditional techniques and advanced digital tools at their disposal.
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(2022)Zia ur Rehman;Muhammad Attique Khan;Fawad Ahmed;Robertas Damaševičius
(2021)Ying Sun;Yaoqing Weng;Bowen Luo;Gongfa Li
(2020)Swalpa Kumar Roy;Shiv Ram Dubey;Subhrasankar Chatterjee;Bidyut Baran Chaudhuri
(2020)Alessandro Ortis;Giovanni Maria Farinella;Sebastiano Battiato
(2020)Dhanamjayulu C;Nizhal U N;Praveen Kumar Reddy Maddikunta;Thippa Reddy Gadekallu
(2021)Xuezhi Xiang;Yuqi Zhang;Abdulmotaleb El Saddik
(2020)Li Huang;Meiling He;Chong Tan;Du Jiang
(2020)Gaurang Bansal;Vinay Chamola;Pratik Narang;Subham Kumar
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