| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 220 | 197 | 220 | 24 |
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing are the subjects of interest in the journal. The journal investigates Artificial intelligence research which frequently intersects with Machine learning. Most of the works presented in Image and Vision Computing deals with Computer vision but it intersects with the subject of Robustness (computer science).
Pattern recognition study tackled is connected to the field of Facial recognition system. Most of the works presented in it deals with Algorithm but it intersects with the subject of Mathematical optimization. Image processing research presented is mostly focused on the subject of Edge detection.
The Image segmentation study tackling the subject of Scale-space segmentation is the focus of it. The Scale-space segmentation research dealing mostly with Segmentation-based object categorization is the focus of Image and Vision Computing.
Artificial intelligence, Computer vision, Pattern recognition, Image processing and Algorithm are the main subjects of interest in the published articles. The published papers aim to address concerns in Artificial intelligence, specifically in the areas of Segmentation, Image (mathematics), Face (geometry), Image segmentation and Facial recognition system. The journal publications explore research in Computer vision and the adjacent study of Robustness (computer science).
The journal mainly tackles studies in Artificial intelligence, Pattern recognition, Feature (computer vision), Computer vision and Machine learning. It focuses on Artificial intelligence research which is adjacent to topics in Task (project management). The research on Pattern recognition featured in Image and Vision Computing combines topics in other fields like Domain (software engineering), Object detection and Benchmark (computing).
The studies on Feature (computer vision) discussed can also contribute to research in the domains of Pixel, Enhanced Data Rates for GSM Evolution, Representation (mathematics) and Boundary (topology). Embedding, Correlation and Robustness (computer science) are some topics wherein Computer vision research discussed in it have an impact. It holds forums on Machine learning that merges themes from other disciplines such as Context (language use), Construct (python library) and Key (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 Image and Vision Computing (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 Image and Vision Computing (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, 4.93% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.37% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.41% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 6.67% of all publications and 75.56% 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 burgeoning field of artificial intelligence, computer vision, and pattern recognition offers prospective job seekers numerous career opportunities. Academia and research are prime sectors seeking experts in these areas, with a high demand for skills in algorithm development and image processing. One such career in academia is that of a private school teacher specializing in computing. Careers in academic institutions offer vast opportunities for growth, such as conducting your own research and getting published in a world-renowned journal. A prime example is the "Image and Vision Computing" journal mentioned previously.
As for any academic position, certain qualifications are required. For instance, some may wonder, "{do private school teachers need a degree in alaska}?" For careers such as a private school computing teacher, there are specific requisites to be fulfilled. Usually, a degree in a relevant field such as Computer Science or Information Technology is crucial, while additional qualifications or specialties, such as those in artificial intelligence, would be advantageous. Most importantly, potential candidates should be prepared to keep learning, evolving and staying ahead of the curve in this rapidly advancing industry.
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(2021)Yifei Zhang;Désiré Sidibé;Olivier Morel;Fabrice Mériaudeau
(2021)Shengkai Wu;Xiaoping Li;Xinggang Wang
(2020)Farhat Afza;Muhammad Attique Khan;Muhammad Sharif;Seifedine Nimer Kadry
(2021)Fan Yang;Xin Chang;Sakriani Sakti;Yang Wu
(2021)Caner Sahin;Guillermo Garcia-Hernando;Juil Sock;Tae-Kyun Kim
(2020)Xuhong Li;Yves Grandvalet;Franck Davoine;Jingchun Cheng
(2020)Kanglin Liu;Guoping Qiu;Wenming Tang;Fei Zhou
(2020)For students exploring computer science education in the USA, online degree programs offer flexibility and accessibility. Many learners benefit from self paced online college courses, allowing them to balance study with work or personal commitments. This approach makes it easier to progress at a comfortable speed while mastering complex topics.
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