Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format
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Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format
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Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format Example of IEEE Transactions on Pattern Analysis and Machine Intelligence format
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IEEE Transactions on Pattern Analysis and Machine Intelligence — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Applied Mathematics #1 of 548 -
Software #1 of 389 up up by 1 rank
Artificial Intelligence #1 of 227 up up by 1 rank
Computational Theory and Mathematics #1 of 133 -
Computer Vision and Pattern Recognition #1 of 85 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 841 Published Papers | 37174 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 06/07/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 5.0
SJR: 0.624
SNIP: 1.866
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 7.3
SJR: 0.73
SNIP: 1.279
open access Open Access

Springer

Quality:  
High
CiteRatio: 7.2
SJR: 0.681
SNIP: 1.299
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.1
SJR: 0.337
SNIP: 0.919

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.

44.2

26% from 2019

CiteRatio for IEEE Transactions on Pattern Analysis and Machine Intelligence from 2016 - 2020
Year Value
2020 44.2
2019 35.2
2018 25.6
2017 20.5
2016 22.0
graph view Graph view
table view Table view

3.811

49% from 2019

SJR for IEEE Transactions on Pattern Analysis and Machine Intelligence from 2016 - 2020
Year Value
2020 3.811
2019 7.536
2018 3.764
2017 2.367
2016 5.388
graph view Graph view
table view Table view

11.215

6% from 2019

SNIP for IEEE Transactions on Pattern Analysis and Machine Intelligence from 2016 - 2020
Year Value
2020 11.215
2019 11.91
2018 9.977
2017 6.299
2016 6.531
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 26% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 49% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 6% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
IEEE Transactions on Pattern Analysis and Machine Intelligence

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IEEE

IEEE Transactions on Pattern Analysis and Machine Intelligence

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope. This includes all traditional areas of computer vision and image understanding...... Read More

Software

Computer Vision and Pattern Recognition

Computational Theory and Mathematics

Artificial Intelligence

Applied Mathematics

Computer Science

i
Last updated on
06 Jul 2020
i
ISSN
0162-8828
i
Impact Factor
Maximum - 8.822
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/TPAMI.1986.4767851
A Computational Approach to Edge Detection
John Canny1

Abstract:

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the so... This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge. read more read less

Topics:

Canny edge detector (64%)64% related to the paper, Edge detection (64%)64% related to the paper, Deriche edge detector (62%)62% related to the paper, Image gradient (62%)62% related to the paper, Sobel operator (58%)58% related to the paper
28,073 Citations
open accessOpen access Journal Article DOI: 10.1109/TPAMI.2016.2577031
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren1, Kaiming He2, Ross Girshick3, Jian Sun2

Abstract:

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN... State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps ( including all steps ) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. read more read less

Topics:

Object detection (58%)58% related to the paper
26,458 Citations
open accessOpen access Journal Article DOI: 10.1109/34.192463
A theory for multiresolution signal decomposition: the wavelet representation
Stéphane Mallat1

Abstract:

Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j ... Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. > read more read less

Topics:

Wavelet (65%)65% related to the paper, Wavelet transform (65%)65% related to the paper, Orthogonal wavelet (65%)65% related to the paper, Wavelet packet decomposition (65%)65% related to the paper, Biorthogonal wavelet (64%)64% related to the paper
View PDF
20,028 Citations
Journal Article DOI: 10.1109/TPAMI.1984.4767596
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman1, Donald Geman2

Abstract:

We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs dist... We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios. read more read less

Topics:

Gibbs sampling (61%)61% related to the paper, Gibbs algorithm (59%)59% related to the paper, Maximum a posteriori estimation (58%)58% related to the paper, Boltzmann distribution (57%)57% related to the paper, Posterior probability (56%)56% related to the paper
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18,761 Citations
Journal Article DOI: 10.1109/34.121791
A method for registration of 3-D shapes
Paul J. Besl1, H.D. McKay1

Abstract:

The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a proc... The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. > read more read less

Topics:

Iterative closest point (63%)63% related to the paper, Point set registration (62%)62% related to the paper, Iterative method (55%)55% related to the paper, Rigid transformation (53%)53% related to the paper, Point (geometry) (52%)52% related to the paper
17,598 Citations
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IEEE Transactions on Pattern Analysis and Machine Intelligence format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Pattern Analysis and Machine Intelligence in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Pattern Analysis and Machine Intelligence guidelines and auto format it.

2. Do you follow the IEEE Transactions on Pattern Analysis and Machine Intelligence guidelines?

Yes, the template is compliant with the IEEE Transactions on Pattern Analysis and Machine Intelligence 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 IEEE Transactions on Pattern Analysis and Machine Intelligence?

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 IEEE Transactions on Pattern Analysis and Machine Intelligence citation style.

4. Can I use the IEEE Transactions on Pattern Analysis and Machine Intelligence 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 IEEE Transactions on Pattern Analysis and Machine Intelligence.

5. Can I use a manuscript in IEEE Transactions on Pattern Analysis and Machine Intelligence 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 IEEE Transactions on Pattern Analysis and Machine Intelligence that you can download at the end.

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It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Transactions on Pattern Analysis and Machine Intelligence.

7. Where can I find the template for the IEEE Transactions on Pattern Analysis and Machine Intelligence?

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 IEEE Transactions on Pattern Analysis and Machine Intelligence's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

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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. IEEE Transactions on Pattern Analysis and Machine Intelligence an online tool or is there a desktop version?

SciSpace's IEEE Transactions on Pattern Analysis and Machine Intelligence 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.

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After writing your paper autoformatting in IEEE Transactions on Pattern Analysis and Machine Intelligence, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Pattern Analysis and Machine Intelligence'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 IEEE Transactions on Pattern Analysis and Machine Intelligence?

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 IEEE Transactions on Pattern Analysis and Machine Intelligence. 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 IEEE Transactions on Pattern Analysis and Machine Intelligence?

The 5 most common citation types in order of usage for IEEE Transactions on Pattern Analysis and Machine Intelligence 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 IEEE Transactions on Pattern Analysis and Machine Intelligence?

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 IEEE Transactions on Pattern Analysis and Machine Intelligence's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on Pattern Analysis and Machine Intelligence 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 IEEE Transactions on Pattern Analysis and Machine Intelligence Endnote style according to Elsevier guidelines.

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