1057-7149
Published by: IEEE
http://signalprocessingsociety.org/publications-resources/ieee-transactions-image-processing
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 4 | 961 | 2149 | 124 |
| Electronics and Electrical Engineering | 90 | 90 | 150 | 33 |
The journal mostly deals with topics like Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing. Feature extraction, Image segmentation, Pixel, Iterative reconstruction and Segmentation are all aspects of Artificial intelligence discussed in the journal. The journal addresses concerns in Feature extraction which are intertwined with other disciplines, such as Object detection, Facial recognition system, Visualization and Machine learning, Convolutional neural network.
Scale-space segmentation and Segmentation-based object categorization are some of the study areas of Image segmentation discussed. Image quality, Image resolution, Image restoration, Data compression and Motion estimation are all subfields of Computer vision research that were featured in IEEE Transactions on Image Processing. While IEEE Transactions on Image Processing focused on Data compression, it was also able to explore topics like Transform coding and Image compression.
IEEE Transactions on Image Processing focuses on Pattern recognition but the discussions also offer insight into other areas such as Contextual image classification, Feature (computer vision) and Robustness (computer science). Some problems in Algorithm that were presented in the journal overlapped with concepts under Wavelet, Wavelet transform, Mathematical optimization and Filter (signal processing). Topics like Edge detection, Digital image and Color image are tackled as part of the discussions on Image processing.
The journal articles investigate areas of study like Artificial intelligence, Computer vision, Image processing, Pattern recognition and Algorithm. The most cited articles deal with Image processing in conjunction with Digital watermarking and similar fields in Watermark. In addition to Algorithm research, the journal papers aim to explore topics under Transform coding and Mathematical optimization.
IEEE Transactions on Image Processing investigates areas of study like Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Feature (computer vision). Most of the Artificial intelligence studies addressed also intersect with Machine learning. It facilitates discussions on Pattern recognition that incorporate concepts from other fields like Object (computer science) and Artificial neural network.
Topics in Feature extraction explored in IEEE Transactions on Image Processing were investigated in conjunction with research in Feature (machine learning), Object detection, Feature learning, Benchmark (computing) and Semantics. IEEE Transactions on Image Processing tackles issues in Computer vision, particularly in the topics of Iterative reconstruction, Image restoration, Pixel, Face (geometry) and RGB color model. IEEE Transactions on Image Processing focuses on Iterative reconstruction but sometimes tackles the closely related topic of Algorithm which is concerned with Robustness (computer science).
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 IEEE Transactions on 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 IEEE Transactions on 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, 3.64% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.44% were posted by at least one author from the top 10 institutions publishing in the journal. Another 17.64% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.00% of all publications and 29.92% 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.
One interesting angle that this analysis could consider is examining some geographic factors, specifically the contribution of Idaho-based authors and institutions. In institutions based in Idaho, there are numerous researchers who have made significant contributions to the above-discussed fields, such as artificial intelligence, image processing, and computer vision. Such authors or researchers who are based in Idaho have not only boosted the state's reputation in the scientific community but have opened many doors for aspiring scholars to get a quality education and make their contribution to the scientific world with a **[teaching certificate in Idaho](https://research.com/careers/cheapest-way-to-get-a-teaching-credential-in-idaho)** in their hands. This profile of Idaho-based authors and institutions could provide a valuable perspective on the potential geographical factors at play in these research fields. We invite you to browse more about the esteemed institutions in Idaho where these researchers have been educated or are currently working. It’s not only fascinating to identify the trends in their contributions but also to ascertain how these can reflect larger patterns of the evolution and future directions of image processing, artificial intelligence and computer vision research. Understanding these factors can be pivotal in setting the path for joining such top-notch researchers.
Yifan Jiang;Xinyu Gong;Ding Liu;Yu Cheng
(2021)Chongyi Li;Chunle Guo;Wenqi Ren;Runmin Cong
(2020)Chongyi Li;Saeed Anwar;Junhui Hou;Runmin Cong
(2021)Jiayi Ma;Han Xu;Junjun Jiang;Xiaoguang Mei
(2020)Kai Wang;Xiaojiang Peng;Jianfei Yang;Debin Meng
(2020)Unknown
(2022)Peng-Tao Jiang;Chang-Bin Zhang;Qibin Hou;Ming-Ming Cheng
(2021)Tao Kong;Fuchun Sun;Huaping Liu;Yuning Jiang
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