Machine Vision and Applications

Top Research Topics at Journal of Machine Vision and Applications?

The journal mostly deals with topics like Artificial intelligence, Computer vision, Pattern recognition, Image (mathematics) and Object (computer science). Artificial intelligence research discussed connects with the study of Computer graphics (images). The research on Computer vision tackled can also make contributions to studies in the areas of Robot, Position (vector) and Robustness (computer science).

Support vector machine is a primary topic of Pattern recognition research in Journal of Machine Vision and Applications. More specifically, the research on Image processing in the journal is related to Digital image processing.

• Artificial intelligence (90.83%)
• Computer vision (60.84%)
• Pattern recognition (20.01%)

What are the most cited papers published in the journal?

• A New Scheme for Practical Flexible and Intelligent Vision Systems (241 citations)
• CHIL - Computers in the Human Interaction Loop. (94 citations)
• Human Activity Recognition Using Sequences of Postures (92 citations)

Research areas of the most cited articles at Journal of Machine Vision and Applications:

The published articles explore disciplines such as Artificial intelligence, Computer vision, Pattern recognition, Image (mathematics) and Robustness (computer science). Many of the studies tackled in the most cited papers connect Artificial intelligence with a similar field of study like Computation. The study on Computer vision presented in the published papers is investigated in conjunction with research in Affine transformation.

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

• Artificial intelligence
• Computer vision
• Operating system

The previous edition focused in particular on these issues:

Journal of Machine Vision and Applications focuses largely on the fields of Artificial intelligence, Computer vision, Pattern recognition, Image (mathematics) and Feature (computer vision). The journal explores research in Artificial intelligence and the adjacent study of Set (abstract data type). The Computer vision works featured in it incorporate elements from Frame (networking) and Computer graphics (images).

While it focused on Pattern recognition, it was also able to explore topics like Machine learning, Small number, Process (computing) and Template matching. The concepts on Image (mathematics) presented in Journal of Machine Vision and Applications can also apply to other research fields, including Algorithm and Data mining. In addition to Feature (computer vision) research, the journal aims to explore topics under Exploit and Representation (mathematics).

The most cited articles from the last journal are:

• Automatic Polyp Detection in Endoscope Images Using a Hessian Filter (17 citations)
• Kinect Unleashed: Getting Control over High Resolution Depth Maps (15 citations)
• An Image-Based System for Change Detection on Tunnel Linings (14 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 Journal of Machine Vision and Applications (based on the number of publications) are:

• Hiroyasu Koshimizu (21 papers) absent at the last edition,
• Masao Sakauchi (17 papers) absent at the last edition,
• Katsushi Ikeuchi (16 papers) published 1 paper at the last edition,
• Roberto Cipolla (15 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Rae-Hong Park (12 papers) absent 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 Journal of Machine Vision and Applications (based on the number of publications) are:

• Osaka University (12 papers) published 3 papers at the last edition the same number as at the previous edition,
• Hitachi (10 papers) absent at the last edition,
• University of Tokyo (10 papers) absent at the last edition,
• University of Tsukuba (9 papers) published 3 papers at the last edition the same number as at the previous edition,
• Nara Institute of Science and Technology (9 papers) published 3 papers at the last 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 2013 edition, 45.45% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.08% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.50% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.75% of all publications and 41.67% 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.

Applications and Career Options in Machine Vision and Applications

The knowledge gained from delving into the complex world of machine vision and applications can potentially lead to various career paths. One popular option is becoming an elementary school teacher in Vermont. In this role, you'll have the opportunity to contribute to the education sector by incorporating the concepts you've learned into your teaching methodologies for students.

Becoming an elementary school teacher in Vermont not only lets you share these exciting fields of AI, computer vision, and pattern recognition, but you can also significantly shape young minds and foster their interest in these innovative areas at an early age. This professional path is especially appealing due to the decent salary packages offered in Vermont.

Moreover, working as an educator allows you to stay connected to these fields, encouraging you to keep up to date with the latest advancements and continuously learn. The intersection of education and these technical fields presents unique career opportunities that you may find rewarding and engaging.

While the path to becoming an educator may not be for everyone, it's just one of the many career options available for individuals interested in Machine Vision and Applications. Therefore, understanding these topics opens doors to several paths you can take to leverage this knowledge for future careers.