2096-0433
Published by: Tsinghua University Press
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 254 | 82 | 107 | 22 |
The journal primarily tackles Computer graphics, Artificial intelligence, Computer vision, Pattern recognition and Image (mathematics). Research in Computer graphics discussed is concerned with the study of Computer graphics (images) as a whole. The study on Artificial intelligence presented in it intersects with the topics under Machine learning.
Computational Visual Media facilitates discussions in 3D reconstruction, Video tracking and RGB color model as part of the larger field of Computer vision, however, it also tackles fields such as Frame (networking). It explores issues in Pattern recognition which can be linked to other research areas like Contextual image classification, Face (geometry) and Benchmark (computing).
The journal articles focus largely on the fields of Computer graphics, Artificial intelligence, Computer vision, Pattern recognition and Data mining. The journal publications address concerns in Computer graphics which are intertwined with other disciplines, such as Visual attention, State (computer science), Information retrieval, Benchmark (computing) and RGB color model. The most cited articles deal with Artificial intelligence in conjunction with Machine learning and similar fields in Visualization.
The journal focuses largely on the fields of Computer graphics, Artificial intelligence, Computer vision, Object (computer science) and Task (project management). The journal facilitated discussions that integrated Computer graphics and Field (computer science). The studies on Artificial intelligence discussed can also contribute to research in the domains of Machine learning and Pattern recognition.
Concepts in Inference, as well as related topics in Inertial motion capture and Skeleton (category theory), are covered in the Pattern recognition research presented in Computational Visual Media. While work presented in it provided substantial information on Object (computer science), it also covered topics in Tracking (particle physics) and Segmentation. Artificial neural network research featured in Computational Visual Media incorporates concerns from various other topics such as Generalization (learning) and Information retrieval.
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 Computational Visual Media (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 Computational Visual Media (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 48.57% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.29% 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 17.14% 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.
A question that often arises is how the relevance of Computational Visual Media extends to practical, everyday applications, such as in teaching. For instance, it may be valuable to incorporate the use of technological tools into course curriculums, such as specifics of artificial intelligence and computer vision for students interested in more advanced learning or even career paths in these areas. Particularly for those educators who aim to be on the frontline of technological advancements in their classroom, understanding the most recent research and trends in Computational Visual Media can be beneficial. Let's consider an example: A high school history teacher in North Carolina might integrate Computational Visual Media in their classroom through graphic visualization tools. This can help students better understand and visualize historical timelines, events, and concepts. This not only modernizes the teaching process but also could enhance students' understanding and retainment of the subject matter. Despite the high technological range of Computational Visual Media topics, they can be transformed into user-friendly tools available to the educator. It can greatly assist in developing course content, projects, and interactive learning experiences, making education more dynamic and engaging. Creating an atmosphere where students can practically apply their knowledge will further help in achieving better academic outcomes. This adoption of innovative tools, methodologies, and thought processes can equip students to navigate the complex world of tomorrow while also fostering a culture of continuous learning and curiosity.
Meng-Hao Guo;Jun-Xiong Cai;Zheng-Ning Liu;Tai-Jiang Mu
(2021)Jun Yuan;Changjian Chen;Weikai Yang;Mengchen Liu
(2021)Tao Zhou;Deng-Ping Fan;Ming-Ming Cheng;Jianbing Shen
(2021)Yifan Xu;Huapeng Wei;Minxuan Lin;Yingying Deng
(2022)Rui Zeng;Yuhui Wen;Wang Zhao;Yong-Jin Liu
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