Example of Pattern Recognition format
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Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format
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Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format Example of Pattern Recognition format
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open access Open Access ISSN: 313203
recommended Recommended

Pattern Recognition — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Software #12 of 389 up up by 12 ranks
Signal Processing #4 of 108 up up by 5 ranks
Artificial Intelligence #9 of 227 up up by 7 ranks
Computer Vision and Pattern Recognition #5 of 85 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1578 Published Papers | 24756 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 14/07/2020
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FAQ

Journal Performance & Insights

  • CiteRatio
  • SJR
  • SNIP

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

15.7

20% from 2019

CiteRatio for Pattern Recognition from 2016 - 2020
Year Value
2020 15.7
2019 13.1
2018 10.4
2017 8.6
2016 9.0
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 20% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

1.492

36% from 2019

SJR for Pattern Recognition from 2016 - 2020
Year Value
2020 1.492
2019 2.323
2018 1.363
2017 1.065
2016 1.501
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 36% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

3.419

6% from 2019

SNIP for Pattern Recognition from 2016 - 2020
Year Value
2020 3.419
2019 3.638
2018 3.211
2017 2.595
2016 3.005
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 6% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

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CiteRatio: 6.7 | SJR: 0.669 | SNIP: 1.739
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CiteRatio: 11.4 | SJR: 1.005 | SNIP: 2.547
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CiteRatio: 7.2 | SJR: 0.681 | SNIP: 1.299
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CiteRatio: 4.1 | SJR: 0.337 | SNIP: 0.919

Pattern Recognition

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Elsevier

Pattern Recognition

Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications...... Read More

Software

Artificial Intelligence

Signal Processing

Computer Vision and Pattern Recognition

Computer Science

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Last updated on
14 Jul 2020
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ISSN
0031-3203
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Impact Factor
High - 2.988
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Acceptance Rate
Not provided
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Frequency
Not provided
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
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Citation Type
Numbered
[25]
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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

Journal Article DOI: 10.1016/0031-3203(95)00067-4
A comparative study of texture measures with classification based on featured distributions
Timo Ojala1, Matti Pietikäinen1, David Harwood2
01 Jan 1996 - Pattern Recognition

Abstract:

This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently For classification a method based on Kullback discrimination of sample and prototype distributions is used The classification results for single fe... This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently For classification a method based on Kullback discrimination of sample and prototype distributions is used The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented read more read less

Topics:

Local binary patterns (53%)53% related to the paper, Feature (machine learning) (53%)53% related to the paper, Texture Descriptor (51%)51% related to the paper
6,070 Citations
open accessOpen access Journal Article DOI: 10.1016/S0031-3203(96)00142-2
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Andrew P. Bradley1
01 Jul 1997 - Pattern Recognition

Abstract:

In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quad... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six ''real world'' medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for ''single number'' evaluation of machine learning algorithms. read more read less

Topics:

Receiver operating characteristic (57%)57% related to the paper, Perceptron (54%)54% related to the paper
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4,289 Citations
open accessOpen access Journal Article DOI: 10.1016/0031-3203(81)90009-1
Generalizing the hough transform to detect arbitrary shapes
Dana H. Ballard1
01 Jan 1987 - Pattern Recognition

Abstract:

The Hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves (1,2) and non-analytic curves, (3) but these methods were restricted to binary edge images. This work was generalized to the detectio... The Hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves (1,2) and non-analytic curves, (3) but these methods were restricted to binary edge images. This work was generalized to the detection of some analytic curves in grey level images, specifically lines, (4) circles (5) and parabolas. (6) The line detection case is the best known of these and has been ingeniously exploited in several applications. (7,8,9) We show how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space. Such a mapping can be exploited to detect instances of that particular shape in an image. Furthermore, variations in the shape such as rotations, scale changes or figure ground reversals correspond to straightforward transformations of this mapping. However, the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes. This makes the generalized Hough transform a kind of universal transform which can be used to find arbitrarily complex shapes. read more read less

Topics:

Generalised Hough transform (73%)73% related to the paper, Randomized Hough transform (71%)71% related to the paper, Hough transform (70%)70% related to the paper, Line (geometry) (51%)51% related to the paper, Image processing (50%)50% related to the paper
View PDF
4,077 Citations
Journal Article DOI: 10.1016/0031-3203(93)90135-J
A review on image segmentation techniques
Nikhil R. Pal1, Sankar K. Pal1
01 Sep 1993 - Pattern Recognition

Abstract:

Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MR... Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MRF) model which is robust to noise, but is computationally involved. Neural network architectures which help to get the output in real time because of their parallel processing ability, have also been used for segmentation and they work fine even when the noise level is very high. The literature on color image segmentation is not that rich as it is for gray tone images. This paper critically reviews and summarizes some of these techniques. Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches. Adequate attention is paid to segmentation of range images and magnetic resonance images. It also addresses the issue of quantitative evaluation of segmentation results. read more read less

Topics:

Scale-space segmentation (73%)73% related to the paper, Image segmentation (71%)71% related to the paper, Segmentation-based object categorization (71%)71% related to the paper, Segmentation (58%)58% related to the paper, Thresholding (56%)56% related to the paper
3,386 Citations
open accessOpen access Journal Article DOI: 10.1016/0031-3203(91)90143-S
Unsupervised texture segmentation using Gabor filters
Anil K. Jain1, Farshid Farrokhnia
01 Dec 1991 - Pattern Recognition

Abstract:

This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selectio... This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories. read more read less

Topics:

Image texture (69%)69% related to the paper, Texture filtering (65%)65% related to the paper, Image segmentation (64%)64% related to the paper, Scale-space segmentation (63%)63% related to the paper, Human visual system model (56%)56% related to the paper
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2,301 Citations
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Time taken to format a paper and Compliance with guidelines

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Pattern Recognition 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

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Pattern Recognition guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Pattern Recognition citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Pattern Recognition's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

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

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Pattern Recognition.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Pattern Recognition Endnote style, according to elsevier guidelines.

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