| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 591 | 72 | 73 | 9 |
The primary areas of discussion in the journal are Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Contextual image classification. Image segmentation, Object detection, Facial recognition system, Video tracking and Segmentation are all aspects of Artificial intelligence discussed in Iet Computer Vision. Scale-space segmentation is the primary subject of Image segmentation works presented in the journal.
The study of Facial recognition system, which falls within the realm of Face (geometry), was the main focus of the presentations. In it, BitTorrent tracker and Eye tracking are investigated in conjunction with one another to address concerns in Video tracking research. In addition to Pattern recognition research, it aims to explore topics under Artificial neural network and Feature (computer vision).
The Computer vision study featured in Iet Computer Vision draws parallels with the field of Robustness (computer science). The studies in Feature extraction featured incorporate elements of Image fusion, Feature vector, Histogram, Deep learning and Image texture. Issues in Contextual image classification were discussed, taking into consideration concepts from other disciplines like Classifier (UML) and Machine learning.
The most cited papers investigate areas of study like Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Contextual image classification. The published papers investigate Artificial intelligence research which frequently intersects with Machine learning. Aside from discussions in Computer vision, the journal articles also deal with the subject of Robustness (computer science) which intersects with Iterative method disciplines.
Iet Computer Vision focuses on Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Segmentation. Algorithm, Speech recognition and Invariant (mathematics) are some topics wherein Artificial intelligence research discussed in it have an impact. The featured works in Similarity (network science), which all belong in the domain if Pattern recognition, also overlaps with concepts under Activity detection.
The featured Computer vision works encompass concepts such as Video tracking, Salient objects and Tracking (particle physics) and examines them in conjunction with Sequence (medicine). Iet Computer Vision focuses on Convolutional neural network but the discussions also offer insight into other areas such as Bilinear interpolation, Segmentation system, Cost sensitive, Sketch recognition and Multi resolution. Some problems in Segmentation that were presented in the journal overlapped with concepts under Pixel, Graph based and Character (mathematics).
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 Computer Vision (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 Computer Vision (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, 6.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 25.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.14% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 23.21% of all publications and 44.64% 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 reading about the diverse range of topics covered in Iet Computer Vision intrigues you and you're considering contributing to the research discussions here, you may wonder how to go about it. Contributing to Iet Computer Vision not only means sharing your insights but also contributing to the enhancement of the knowledge in the fields of Artificial Intelligence, Computer Vision, and Pattern Recognition. As a researcher, first, you need to align your area of interest to the key discussion topics of the journal. Look for gaps in the current research articles and identify how your research can fill those gaps. Make sure your research incorporates aspects of artificial intelligence, pattern recognition or computer vision; which are the primary focuses of the journal. Next, you need to prepare your research paper in accordance with the guidelines provided by the journal. Ensure your paper contributes original, high-quality research which adheres to the ethical standards of the scientific community. Before submitting your paper, it is critical to proofread and check the format of your work. You may also want to get your paper peer-reviewed for crucial feedback. Once your paper is ready for submission, follow the submission guidelines carefully. Lastly, continuous learning and development are key. Keep yourself updated with ongoing trends in the field of your research. Participating in conferences, workshops, and taking relevant courses can be an effective way. Being part of the academic community, especially in a field impacting our daily lives like artificial intelligence and machine learning, is indeed rewarding. Not sure where to start? For instance, you can explore how do you become a preschool teacher in South Dakota and start your journey from there, continuously expanding your areas of expertise and contributing to growing fields like computer vision. As with all other research fields, the journey is challenging but it brings about great personal satisfaction and contributes significantly to the advancement of knowledge and understanding.
Wolfgang Paier;Anna Hilsmann;Peter Eisert
(2020)S. Alkassar;Bilal A. Jebur;Mohammed A. M. Abdullah;Joanna H. Al-Khalidy
(2021)Konstantinos A. Tsintotas;Loukas Bampis;Antonios Gasteratos
(2021)Federico Pollastri;Mario Parreño;Juan Maroñas;Federico Bolelli
(2021)Anna Hilsmann;Philipp Fechteler;Wieland Morgenstern;Wolfgang Paier
(2020)Xiang Zhang;Lei Tang;Hangzai Luo;Sheng Zhong
(2021)Roziana Ramli;Mohd Yamani Idna Bin Idris;Khairunnisa Hasikin;Noor Khairiah A. Karim
(2020)Haohua Zhao;Weichen Xue;Xiaobo Li;Zhangxuan Gu
(2020)Claudio Piciarelli;Gian Luca Foresti
(2020)Cheng Peng;Haozhi Huang;Ah Chung Tsoi;Sio-Long Lo
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