| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1004 | 7 | 11 | 3 |
The journal facilitates discussions on Pattern recognition (psychology), Artificial intelligence, Computer vision, Pattern recognition and Algorithm. Issues in Pattern recognition (psychology) were discussed, taking into consideration concepts from other disciplines like Basis (linear algebra), Data mining, Set (abstract data type), Object (computer science) and Image (mathematics). The Artificial intelligence study tackled is a key component of adjacent topics in the area of Machine learning.
Digital image is a focus of the Computer vision works in Pattern Recognition and Image Analysis. While work presented in Pattern Recognition and Image Analysis provided substantial information on Pattern recognition, it also covered topics in Feature (computer vision) and Cluster analysis. The in-depth study on Algorithm also explores topics in the intersecting field of Mathematical optimization.
It features studies on Image processing, including topics such as Digital image processing. Scale-space segmentation and Image segmentation are Segmentation topics of special interest in the journal.
The journal publications mainly deal with areas of study such as Artificial intelligence, Pattern recognition (psychology), Computer vision, Pattern recognition and Image (mathematics). Artificial intelligence research in the published papers connects with the study of Machine learning. While the published articles focused on Pattern recognition (psychology), they were also able to explore topics like Dimension (vector space), Feature (computer vision), Face (geometry), Set (abstract data type) and Algorithm.
The journal covers a variety of subjects, including Pattern recognition (psychology), Artificial intelligence, Pattern recognition, Convolutional neural network and Image (mathematics). It facilitates discussions on Pattern recognition (psychology) that incorporate concepts from other fields like Segmentation, Image processing, Artificial neural network, Field (computer science) and Algorithm. The work on Artificial intelligence tackled in it brings together disciplines like Process (computing) and Computer vision.
While it focused on Pattern recognition, it was also able to explore topics like Iris recognition, Texture (music), Noise (video), Cluster analysis and Facial recognition system. Convolutional neural network research presented in the journal encompasses a variety of subjects, including Transfer of learning, Set (abstract data type), Feature (machine learning) and Overfitting. In it, Smoothing, Layer (object-oriented design) and Rotation (mathematics) are investigated in conjunction with one another to address concerns in Image (mathematics) research.
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 Pattern Recognition and Image Analysis (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 Pattern Recognition and Image Analysis (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, 7.14% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.38% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.85% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.31% of all publications and 38.46% 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.
Becoming a significant contributor to the Pattern Recognition and Image Analysis Journal requires more than just academic knowledge of topics like Artificial Intelligence, Machine Learning, and Computer Vision. Apart from proficiency in these subjects, aspiring researchers need to have practical experience, often conducted by assisting established professionals in their field. For instance, if you're looking to enter the educational facet of this field, beginning as a preschool teacher assistant can offer valuable experience. If you are located in North Carolina, it is essential to know the specific teacher assistant certificate requirements in North Carolina, which may vary from other states. Understanding these requirements and meeting them can be your stepping stone into the education field, ultimately enhancing your understanding of Pattern Recognition and Image Analysis concepts in practical applications. An academic and practical background, in combination, will greatly improve your research quality and ability to contribute valuable insight within this academic community.
Aman Agarwal;Aditya Mishra;Madhushree Basavarajaiah;Priyanka Sharma
(2021)Zhitong Huang;Ching Yee Suen
(2021)Bernd Radig;Paul Bodesheim;Dimitri Korsch;Joachim Denzler
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