| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1002 | 7 | 12 | 3 |
The aim of International Journal of Computational Vision and Robotics is to expand the discussion of research in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image processing. The study on Artificial intelligence presented in it intersects with the topics under Machine learning. It investigates Computer vision research which frequently intersects with Mobile robot.
It addresses concerns in Pattern recognition which are intertwined with other disciplines, such as Histogram and Image retrieval. The journal emphasizes research on Image retrieval, which includes concerns such as Content-based image retrieval. In the journal, Speech recognition, Feature (computer vision) and Natural language processing are investigated in conjunction with one another to address concerns in Feature extraction research.
The research on Image processing discussed in the journal draws on the closely related field of Image segmentation. Motion planning and Robot control are among the concentrations of Robot that garnered much attention in it.
The journal publications focus on Artificial intelligence, Computer vision, Pattern recognition, Machine vision and Face (geometry). While Artificial intelligence is the focus of the journal publications, it also provides insights into the studies of Field (computer science) and Expression (mathematics). The most cited publications hold forums on Pattern recognition that merge themes from other disciplines such as Information extraction and Image (mathematics), Image retrieval.
The journal mainly deals with areas of study such as Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Machine learning. International Journal of Computational Vision and Robotics covers various topics on Artificial intelligence such as Convolutional neural network, Image processing, Support vector machine, Artificial neural network and Noise reduction. Similarity (network science), Feature extraction and Segmentation studies in the realm of Pattern recognition interact with fields like Exudate.
Some problems in Computer vision that were presented in International Journal of Computational Vision and Robotics overlapped with concepts under Ultrasound and Euclidean distance. Deep learning research featured in the journal incorporates concerns from various other topics such as Speech recognition and Gesture recognition. Topics in Machine learning explored in it were investigated in conjunction with research in Classifier (UML), Software, Software development and Malware.
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 International Journal of Computational Vision and Robotics (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 International Journal of Computational Vision and Robotics (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, 46.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.33% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.67% of all publications and 58.33% 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.
If you are interested in areas such as Artificial Intelligence, Pattern Recognition, or Computer Vision, you might want to consider a career in education or academia. There are several ways to obtain a teaching credential in the United States. Each state has differing requirements and it is important to research the most affordable and convenient options for your situation and career goals.
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Within an academic setting, you will not only have the opportunity to teach future generations of students but also conduct research in areas highlighted in The International Journal of Computational Vision and Robotics. Professors often have access to a wealth of resources to explore their academic interests further and bring their knowledge back to the classroom. Exploring career possibilities in academia enables you to remain involved in your field of interest, contribute to its ongoing research, and inspire new minds to do the same.
Amjad Rehman
(2020)Amjad Rehman;Majid Harouni;Tanzila Saba
(2020)Saba Joudaki;Amjad Rehman
(2022)Sushma Rani Martha;Ganapati Panda;Manorama Patri
(2022)Qiaokang Liang;Shao Xiang;Jianyong Long;Dan Zhang
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