Example of Computer Vision and Image Understanding format
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Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format
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Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format Example of Computer Vision and Image Understanding format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

Computer Vision and Image Understanding — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Software #37 of 389 up up by 21 ranks
Computer Vision and Pattern Recognition #9 of 85 up up by 2 ranks
Signal Processing #12 of 108 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 429 Published Papers | 4276 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 12/06/2020
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Related Journals

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IEEE

Quality:  
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CiteRatio: 11.4
SJR: 1.005
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CiteRatio: 15.7
SJR: 1.492
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CiteRatio: 6.3
SJR: 0.544
SNIP: 1.494
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 6.7
SJR: 0.669
SNIP: 1.739

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

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

3.121

18% from 2018

Impact factor for Computer Vision and Image Understanding from 2016 - 2019
Year Value
2019 3.121
2018 2.645
2017 2.391
2016 2.498
graph view Graph view
table view Table view

10.0

15% from 2019

CiteRatio for Computer Vision and Image Understanding from 2016 - 2020
Year Value
2020 10.0
2019 8.7
2018 6.9
2017 6.0
2016 6.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 18% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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.

0.854

41% from 2019

SJR for Computer Vision and Image Understanding from 2016 - 2020
Year Value
2020 0.854
2019 1.453
2018 0.766
2017 0.717
2016 1.297
graph view Graph view
table view Table view

2.059

9% from 2019

SNIP for Computer Vision and Image Understanding from 2016 - 2020
Year Value
2020 2.059
2019 2.255
2018 2.039
2017 1.907
2016 2.143
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 41% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

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

Computer Vision and Image Understanding

Guideline source: View

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Elsevier

Computer Vision and Image Understanding

The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of re...... Read More

Software

Computer Vision and Pattern Recognition

Signal Processing

Computer Science

i
Last updated on
11 Jun 2020
i
ISSN
1077-3142
i
Impact Factor
High - 2.729
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
i
Citation Type
Numbered
[25]
i
Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1016/J.CVIU.2007.09.014
Speeded-Up Robust Features (SURF)
Herbert Bay1, Andreas Ess1, Tinne Tuytelaars2, Luc Van Gool1

Topics:

GLOH (58%)58% related to the paper, Principal curvature-based region detector (54%)54% related to the paper, Hessian affine region detector (54%)54% related to the paper, Interest point detection (52%)52% related to the paper, Robustness (computer science) (52%)52% related to the paper
View PDF
12,449 Citations
Journal Article DOI: 10.1006/CVIU.1995.1004
Active shape models—their training and application

Topics:

Active shape model (66%)66% related to the paper, Active appearance model (65%)65% related to the paper, Active contour model (59%)59% related to the paper, Point distribution model (58%)58% related to the paper, Image processing (52%)52% related to the paper
View PDF
7,969 Citations
Journal Article DOI: 10.1016/J.CVIU.2006.08.002
A survey of advances in vision-based human motion capture and analysis
Thomas B. Moeslund1, Adrian Hilton2, Volker Krüger3

Topics:

Body movement (54%)54% related to the paper, Pose (53%)53% related to the paper, Motion analysis (53%)53% related to the paper
View PDF
2,738 Citations
Journal Article DOI: 10.1016/J.CVIU.2005.09.012
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Li Fei-Fei1, Rob Fergus2, Pietro Perona3

Topics:

Generative model (57%)57% related to the paper, Learning object (55%)55% related to the paper, Caltech 101 (54%)54% related to the paper, Bayesian inference (53%)53% related to the paper, Cognitive neuroscience of visual object recognition (51%)51% related to the paper
2,597 Citations
Journal Article DOI: 10.1006/CVIU.1999.0832
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Philip H. S. Torr1, Andrew Zisserman2

Topics:

RANSAC (58%)58% related to the paper, Constrained optimization (54%)54% related to the paper, Estimator (54%)54% related to the paper, Robust statistics (52%)52% related to the paper, Real image (52%)52% related to the paper
View PDF
2,267 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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With SciSpace, you do not need a word template for Computer Vision and Image Understanding.

It automatically formats your research paper to Elsevier formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

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Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
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Computer Vision and Image Understanding format uses elsarticle-num citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Computer Vision and Image Understanding in LaTeX?

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

2. Do you follow the Computer Vision and Image Understanding guidelines?

Yes, the template is compliant with the Computer Vision and Image Understanding 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 Computer Vision and Image Understanding?

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 Computer Vision and Image Understanding citation style.

4. Can I use the Computer Vision and Image Understanding 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 Computer Vision and Image Understanding.

5. Can I use a manuscript in Computer Vision and Image Understanding 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 Computer Vision and Image Understanding that you can download at the end.

6. How long does it usually take you to format my papers in Computer Vision and Image Understanding?

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

7. Where can I find the template for the Computer Vision and Image Understanding?

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 Computer Vision and Image Understanding'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 Computer Vision and Image Understanding'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. Computer Vision and Image Understanding an online tool or is there a desktop version?

SciSpace's Computer Vision and Image Understanding 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 Computer Vision and Image Understanding?

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 Computer Vision and Image Understanding?”

11. What is the output that I would get after using Computer Vision and Image Understanding?

After writing your paper autoformatting in Computer Vision and Image Understanding, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computer Vision and Image Understanding'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 Computer Vision and Image Understanding?

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 Computer Vision and Image Understanding. 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 Computer Vision and Image Understanding?

The 5 most common citation types in order of usage for Computer Vision and Image Understanding 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 Computer Vision and Image Understanding?

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 Computer Vision and Image Understanding's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Computer Vision and Image Understanding 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 Computer Vision and Image Understanding Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

Andreas Frutiger
Researcher & Ex MS Word user
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