ISSN: 1751-9632

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IET Computer Vision Q3 Unclaimed

John Wiley & Sons Inc. United Kingdom

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IET Computer Vision is a journal indexed in SJR in Software and Computer Vision and Pattern Recognition with an H index of 47. It has an SJR impact factor of 0,443 and it has a best quartile of Q3. It has an SJR impact factor of 0,443.

Type: Journal

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Open Access Policy:

Type of publications:

Publication frecuency: -

Categories: Computer Vision and Pattern Recognition (Q3) Software (Q3)

Price

- €

Inmediate OA

NPD

Embargoed OA

- €

Non OA

Metrics

0,443

SJR Impact factor

47

H Index

110

Total Docs (Last Year)

191

Total Docs (3 years)

5193

Total Refs

406

Total Cites (3 years)

186

Citable Docs (3 years)

2.14

Cites/Doc (2 years)

47.21

Ref/Doc

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Aims and Scope

Best articles by citations

Edge detection based on the Shannon Entropy by piecewise thresholding on remote sensing images

Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description

Statistical analysis of three-dimensional optical flow separability in volumetric images

Total contribution score and fuzzy entropy based two-stage selection of FC, ReLU and inverseReLU features of multiple convolution neural networks for erythrocytes detection

Two-person activity recognition using skeleton data

Effective background modelling and subtraction approach for moving object detection

Self-adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy

Method for unconstrained text detection in natural scene image

Relay dueling network for visual tracking with broad field-of-view

A quadratic programming approach to image labelling

Augmented Lagrangian-based approach for dense three-dimensional structure and motion estimation from binocular image sequences

Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer

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Flower classification using deep convolutional neural networks

Compact descriptor for local feature using dominating centre-symmetric local binary pattern

Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds

Compact discriminative object representation via weakly supervised learning for real-time visual tracking

Curve description by histograms of tangent directions

Efficient levels of spatial pyramid representation for local binary patterns

Mixing body-parts model for 2D human pose estimation in stereo videos

Performance evaluation of local features in human classification and detection

Feedback cascade regression model for face alignment

Video summarisation by deep visual and categorical diversity

Learning multi-planar scene models in multi-camera videos

Addressing the illumination challenge in two-dimensional face recognition: a survey