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IEEE Transactions on Pattern Analysis and Machine Intelligence is a peer-reviewed journal published by IEEE, covering Advanced Vision and Imaging, Advanced Image and Video Retrieval Techniques, Image Retrieval and Classification Techniques. Over its lifetime it has published 12,392 papers, which have received 1,872,233 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Pattern Analysis and Machine Intelligence style. No sign-up. |
| Publishes research in | Advanced Vision and Imaging Advanced Image and Video Retrieval Techniques Image Retrieval and Classification Techniques Robotics and Sensor-Based Localization Medical Image Segmentation Techniques |
| ISSN | 0162-8828 |
| Citation impact (2-yr) | 12.36 |
| h-index | 551 |
| i10-index | 8,035 |
| Top institutions publishing here | Tsinghua University |
| Journal website | www.computer.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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…
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…
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution…
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…
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…
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