1077-3142
Published by: Elsevier
https://www.journals.elsevier.com/computer-vision-and-image-understanding
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 171 | 265 | 290 | 29 |
Computer Vision and Image Understanding aims to foster the development of research in Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing. Artificial intelligence study tackled is connected to the field of Machine learning. The Computer vision study tackled is a key component of adjacent topics in the area of Robustness (computer science).
The study on Pattern recognition presented is investigated in conjunction with research in Cluster analysis. The work on Algorithm addressed in Computer Vision and Image Understanding expands to the thematically related Mathematical optimization. The Image processing works, particularly on Edge detection are tackled in it.
The study on Image segmentation featured in the journal expounds on the topic of Scale-space segmentation in particular. It emphasizes research on Scale-space segmentation, which includes concerns such as Segmentation-based object categorization.
The most cited publications are organized to address concerns in the fields of Artificial intelligence, Computer vision, Image processing, Pattern recognition and Algorithm. The journal articles connects the study in Artificial intelligence with the closely related areas of Machine learning. The published articles investigate Computer vision research which frequently intersects with Pattern recognition (psychology).
Computer Vision and Image Understanding primarily focuses on research topics in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Image (mathematics). Artificial intelligence studies presented in the journal focus on topics such as Deep learning, Convolutional neural network, Segmentation, Artificial neural network and Feature (computer vision). The studies tackled, which mainly focus on Computer vision, apply to Robustness (computer science) as well.
The journal explores topics in Pattern recognition which can be helpful for research in disciplines like Context (language use), Simple (abstract algebra), Noise reduction and Overfitting. The research on Machine learning tackled can also make contributions to studies in the areas of Graph (abstract data type), Contextual image classification, Object (computer science), Structure (mathematical logic) and Benchmark (computing). The presented Image (mathematics) research focuses mostly on Face (geometry) and, on occasion, topics in Landmark.
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 Computer Vision and Image Understanding (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 Computer Vision and Image Understanding (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, 3.06% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 9.47% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.32% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.74% of all publications and 69.47% 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.
The field of artificial intelligence and computer vision holds promising career prospects. Various job roles, including research analysts, data scientists, machine learning engineers, and educators, are making significant contributions to the advancements in this domain. Particularly, the role of educators in molding the future of AI and computer vision is unparalleled. For instance, high school art teachers often embrace digital technology to integrate AI and computer vision in their design and art curriculum. In doing so, they are playing a pivotal role in fostering the imaginative abilities and critical thinking skills of the youth. If you wish to make a career in such a fulfilling role, we recommend reading our guide on how to become a high school art teacher in Montana, which provides useful insights into the educational requirements, skill sets, and job prospects for aspiring art teachers in Montana. In conclusion, careers in AI and computer vision are not only limited to technical roles; there is ample scope in areas like academics and education as well. The journey towards becoming an effective educator in this field requires a blend of passion for art, understanding of digital technologies, and dedication towards influencing young minds positively. This all begins with appropriate steps towards acquiring necessary education and skills.
Longyin Wen;Dawei Du;Zhaowei Cai;Zhen Lei
(2020)Yucheng Chen;Yingli Tian;Mingyi He
(2020)Jinbao Wang;Shujie Tan;Xiantong Zhen;Shuo Xu
(2021)Chiara Plizzari;Chiara Plizzari;Marco Cannici;Matteo Matteucci
(2021)Yaxiang Fan;Yaxiang Fan;Gongjian Wen;Deren Li;Shaohua Qiu;Shaohua Qiu
(2020)Zhiqing Guo;Gaobo Yang;Jiyou Chen;Xingming Sun
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