IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence is a journal indexed in SJR in Software and Applied Mathematics with an H index of 435. It has a COP Index of 22,2 (best band D1). It is published in English. It has a COP Index of 22,2.
IEEE Transactions on Pattern Analysis and Machine Intelligence focuses its scope in these topics and keywords: motion, shape, matching, applications, image, d, data, depth, efficient, images, ...
1 of 503 journals in Applied Mathematics
Metrics
Scimago and CountryOfPapers database fields
COP Index
22,2
D1 Top 10%
SJR Impact
3,91
H-index
435
Docs (year)
812
Docs 3y
2070
Total refs
65710
Cites 3y
47377
Citable 3y
2059
Cites/Doc 2y
21.19
Ref/Doc
80.92
Immediate OA
—
Embargoed OA
NPD
Non OA / Submission
—
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Researcher reviews
Best articles by citations
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
A theory for multiresolution signal decomposition: the wavelet representation
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Scale-space and edge detection using anisotropic diffusion
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
A model of saliency-based visual attention for rapid scene analysis
Mean shift: a robust approach toward feature space analysis
Representation Learning: A Review and New Perspectives
Robust Face Recognition via Sparse Representation
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Object Detection with Discriminatively Trained Part-Based Models
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Image Super-Resolution Using Deep Convolutional Networks
Fast approximate energy minimization via graph cuts
Fully Convolutional Networks for Semantic Segmentation