Signal, Image and Video Processing

Top Research Topics at Signal, Image and Video Processing?

The journal facilitates discussions on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Multimedia information systems. It covers various topics on Artificial intelligence such as Image (mathematics), Segmentation, Pixel, Feature (computer vision) and Feature extraction. Research on Pattern recognition presented in Signal, Image and Video Processing focuses, in particular, on Support vector machine, Convolutional neural network, Image segmentation, Wavelet and Feature vector.

The Wavelet study tackling the subject of Wavelet transform is the focus of the journal. The studies tackled, which mainly focus on Computer vision, apply to Robustness (computer science) as well. The concepts on Algorithm presented in it can also apply to other research fields, including Signal, Mathematical optimization, Coding (social sciences) and Noise.

• Artificial intelligence (62.00%)
• Pattern recognition (33.72%)
• Computer vision (33.12%)

What are the most cited papers published in the journal?

• Image fusion based on pixel significance using cross bilateral filter (228 citations)
• A survey on super-resolution imaging (205 citations)
• Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network (159 citations)

Research areas of the most cited articles at Signal, Image and Video Processing:

The journal papers are organized to address concerns in the fields of Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Multimedia information systems. The journal publications address concerns in Pattern recognition which are intertwined with other disciplines, such as Local binary patterns and Biometrics. While the journal publications focused on Algorithm, they were also able to explore topics like Noise, Signal, Filter (signal processing) and Control theory.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Computer vision

The previous edition focused in particular on these issues:

The objective of Signal, Image and Video Processing is to combine knowledge in the areas of Artificial intelligence, Pattern recognition, Algorithm, Image (mathematics) and Computer vision. It concentrated on Artificial intelligence research, specifically Convolutional neural network, Feature (computer vision), Segmentation, Deep learning and Multimedia information systems. Topics in Pattern recognition explored in it were investigated in conjunction with research in Artificial neural network, Cluster analysis and Benchmark (computing).

In it, Norm (mathematics) and Signal are investigated in conjunction with one another to address concerns in Algorithm research. Issues in Image (mathematics) were discussed, taking into consideration concepts from other disciplines like Computational complexity theory, Pixel and Residual. It concentrates on Computer vision topics that focus on Image quality and Object detection.

The most cited articles from the last journal are:

• Image denoising via deep residual convolutional neural networks (8 citations)
• Joint training of two-channel deep neural network for brain tumor classification (7 citations)
• Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. (6 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Signal, Image and Video Processing (based on the number of publications) are:

• A. Enis Cetin (11 papers) absent at the last edition,
• Hasan Demirel (9 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Bingo Wing-Kuen Ling (8 papers) published 3 papers at the last edition,
• Bingo Wing-Kuen Ling (8 papers) absent at the last edition,
• Madhu S. Nair (7 papers) published 2 papers at the last edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Signal, Image and Video Processing (based on the number of publications) are:

• Harbin Institute of Technology (42 papers) published 8 papers at the last edition, 3 more than at the previous edition,
• Chinese Academy of Sciences (28 papers) published 4 papers at the last edition, 1 more than at the previous edition,
• Islamic Azad University (27 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Xidian University (25 papers) published 1 paper at the last edition, 2 less than at the previous edition,
• Eastern Mediterranean University (25 papers) published 4 papers at the last edition, 2 more than at the previous edition.

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.

Publication chance based on affiliation

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, 13.69% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.70% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.38% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.65% of all publications and 68.27% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities in the Field of Signal, Image and Video Processing

Even as we dive into the fascinating research topics, notable papers, and emerging trends in the arena of Signal, Image, and Video Processing, it is equally important to acknowledge the career implications of this field. Various professionals, from engineers and data scientists to educators and analysts, can reap enormous value from gaining expertise in this area. A robust understanding of concepts such as Artificial Intelligence, Pattern Recognition, and Computer Vision, among others, can advance one's career in innumerable ways. To illustrate, consider the role of a Preschool Teacher Assistant. While it may seem unrelated at first, one must note that the contemporary educational landscape is increasingly integrating technology in teaching methodologies. With foundational knowledge in Signal, Image, and Video Processing, preschool teacher assistants could contribute to creating an immersive and interactive learning experience for young minds using multimedia and other digital tools. If you may be interested, here is a helpful guide on how to become a preschool teacher assistant in Tennessee. Of course, opportunities are not limited to the field of education. Professionals engaged in sectors such as technology, media, security, defense, and healthcare can harness the power of Signal, Image, and Video Processing to spearhead significant advancements in their respective fields. The accelerating digital transformation in every industry underscores the increasing demand for such skillsets. To conclude, investing time and effort into understanding Signal, Image, and Video Processing indeed offers rewarding career prospects along with a compelling academic journey. Remember, the pursuit of knowledge not only enriches our understanding of the world but also opens a world of possibilities for personal and professional growth.