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 an SJR impact factor of 3,91 and it has a best quartile of Q1. It is published in English. It has an SJR impact factor of 3,91.
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, ...
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Metrics
Campos Scimago / CoP — sin series inventadas
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