Example of Journal of Imaging format
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Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format
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Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format Example of Journal of Imaging format
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open access Open Access

Journal of Imaging — Template for authors

Categories Rank Trend in last 3 yrs
Radiology, Nuclear Medicine and Imaging #61 of 288 down down by None rank
Computer Graphics and Computer-Aided Design #22 of 88 down down by None rank
Electrical and Electronic Engineering #181 of 693 down down by None rank
Computer Vision and Pattern Recognition #27 of 85 down down by None rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 404 Published Papers | 1925 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/06/2020
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.8
SJR: 1.033
SNIP: 1.943
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Elsevier

Quality:  
High
CiteRatio: 24.2
SJR: 2.887
SNIP: 5.246
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Springer

Quality:  
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CiteRatio: 8.6
SJR: 0.53
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

4.8

55% from 2019

CiteRatio for Journal of Imaging from 2016 - 2020
Year Value
2020 4.8
2019 3.1
2018 1.3
graph view Graph view
table view Table view

0.538

11% from 2019

SJR for Journal of Imaging from 2019 - 2020
Year Value
2020 0.538
2019 0.485
graph view Graph view
table view Table view

1.331

9% from 2019

SNIP for Journal of Imaging from 2018 - 2020
Year Value
2020 1.331
2019 1.226
2018 0.753
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 55% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 11% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Imaging

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Multidisciplinary Digital Publishing Institute

Journal of Imaging

The Journal of Imaging (ISSN 2313-433X) is an international, multi/interdisciplinary, open access, peer-reviewed journal which publishes reviews, original research papers, communications, case reports, letters, and short notes in all fields of imaging research. There is no res...... Read More

i
Last updated on
10 Jun 2020
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ISSN
2313-433X
i
Acceptance Rate
75%
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Frequency
4 issues/year
i
Open Access
Not provided
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E.; Tinkham, M.; Klapwijk, T.M. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 1982, 25, 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.3390/JIMAGING6060052
Explainable Deep Learning Models in Medical Image Analysis.
Amitojdeep Singh1, Sourya Sengupta1, Vasudevan Lakshminarayanan1
20 Jun 2020 - Journal of Imaging

Abstract:

Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those However, the black-box nature of the algorithms has restricted their clinical use Recent explainability studies aim to show the features that influence the decision of a model the ... Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those However, the black-box nature of the algorithms has restricted their clinical use Recent explainability studies aim to show the features that influence the decision of a model the most The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations A review of the current applications of explainable deep learning for different medical imaging tasks is presented here The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users read more read less
298 Citations
open accessOpen access Journal Article DOI: 10.3390/JIMAGING4020036
An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos
07 Feb 2018 - Journal of Imaging

Abstract:

Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorize... Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection. read more read less

Topics:

Anomaly detection (61%)61% related to the paper, Supervised learning (59%)59% related to the paper, Feature learning (56%)56% related to the paper, Deep learning (55%)55% related to the paper
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287 Citations
open accessOpen access Journal Article DOI: 10.3390/JIMAGING6080073
Hand Gesture Recognition Based on Computer Vision: A Review of Techniques
Munir Oudah, Ali Al-Naji1, Javaan Chahl1
23 Jul 2020 - Journal of Imaging

Abstract:

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based ... Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications. read more read less

Topics:

Gesture recognition (72%)72% related to the paper, Gesture (59%)59% related to the paper
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232 Citations
open accessOpen access Journal Article DOI: 10.3390/JIMAGING5050052
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
Alberto Signoroni1, Mattia Savardi1, Annalisa Baronio1, Sergio Benini1
08 May 2019 - Journal of Imaging

Abstract:

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and ... Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends. read more read less

Topics:

Hyperspectral imaging (52%)52% related to the paper
184 Citations
open accessOpen access Journal Article DOI: 10.3390/JIMAGING3020021
Object Recognition in Aerial Images Using Convolutional Neural Networks
Matija Radovic, Offei Adarkwa, Qiaosong Wang
14 Jun 2017 - Journal of Imaging

Abstract:

There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decisio... There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time. read more read less

Topics:

Convolutional neural network (57%)57% related to the paper
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147 Citations
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Frequently asked questions

1. Can I write Journal of Imaging in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Imaging guidelines and auto format it.

2. Do you follow the Journal of Imaging guidelines?

Yes, the template is compliant with the Journal of Imaging guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Imaging?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Imaging citation style.

4. Can I use the Journal of Imaging templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Imaging.

5. Can I use a manuscript in Journal of Imaging that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Imaging that you can download at the end.

6. How long does it usually take you to format my papers in Journal of Imaging?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Journal of Imaging.

7. Where can I find the template for the Journal of Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of Imaging's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Journal of Imaging an online tool or is there a desktop version?

SciSpace's Journal of Imaging is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Imaging?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Journal of Imaging?”

11. What is the output that I would get after using Journal of Imaging?

After writing your paper autoformatting in Journal of Imaging, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Imaging's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Imaging?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Imaging. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Imaging?

The 5 most common citation types in order of usage for Journal of Imaging are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Journal of Imaging in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Imaging Endnote style according to Elsevier guidelines.

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