IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Read the full scope of TPAMI.
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From the August 2018 issue
Gaussian Process Morphable Models
By Marcel Luthi, Thomas Gerig, Christoph Jud, and Thomas Vetter
Models of shape variations have become a central component for the automated analysis of images. An important class of shape models are point distribution models (PDMs). These models represent a class of shapes as a normal distribution of point variations, whose parameters are estimated from example shapes. Principal component analysis (PCA) is applied to obtain a low-dimensional representation of the shape variation in terms of the leading principal components. In this paper, we propose a generalization of PDMs, which we refer to as Gaussian Process Morphable Models (GPMMs). We model the shape variations with a Gaussian process, which we represent using the leading components of its Karhunen-Loève expansion. To compute the expansion, we make use of an approximation scheme based on the Nyström method. The resulting model can be seen as a continuous analog of a standard PDM. However, while for PDMs the shape variation is restricted to the linear span of the example data, with GPMMs we can define the shape variation using any Gaussian process. For example, we can build shape models that correspond to classical spline models and thus do not require any example data. Furthermore, Gaussian processes make it possible to combine different models. For example, a PDM can be extended with a spline model, to obtain a model that incorporates learned shape characteristics but is flexible enough to explain shapes that cannot be represented by the PDM. We introduce a simple algorithm for fitting a GPMM to a surface or image. This results in a non-rigid registration approach whose regularization properties are defined by a GPMM. We show how we can obtain different registration schemes, including methods for multi-scale or hybrid registration, by constructing an appropriate GPMM. As our approach strictly separates modeling from the fitting process, this is all achieved without changes to the fitting algorithm. To demonstrate the applicability and versatility of GPMMs, we perform a set of experiments in typical usage scenarios in medical image analysis and computer vision: The model-based segmentation of 3D forearm images and the building of a statistical model of the face. To complement the paper, we have made all our methods available as open source.
Editorials and Announcements
- TPAMI now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
- We are pleased to announce that Sven Dickinson, a professor in the Department of Computer Science at the University of Toronto, Canada, has been named the new Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence starting in 2017.
- According to Clarivate Analytics' 2016 Journal Citation Report, TPAMI has an impact factor of 8.329.
- State of the Journal (Jan 2018)
- Incoming EIC Editorial (Jan 2017)
- State of the Journal (Jan 2017)
- State of the Journal (Feb 2016)
- State of the Journal (Jan 2015)
- Editor's Note (June 2013)
- Farewall State of the Journal (Jan 2013)
- Editor's Note (Jan 2013)
- Editor's Note (May 2012)
- Editor's Note (February 2012)
- State of the Journal (January 2012)
- Guest Editors' Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis (May 2018)
- Best of CVPR 2015 (April 2017)
- Special Issue on Multimodal Human Pose Recovery and Behavior Analysis (August 2016)
- Special Section on CVPR 2014 (July 2016)
- Special Section on CVPR 2013 (April 2016)
- Special Issue on Higher Order Graphical Models in Computer Vision (July 2015)
- Special Issue on Bayesian Nonparametrics (Feb 2015)
- TPAMI CVPR Special Section (Dec 2013)
- Special Section on Learning Deep Architectures (Aug 2013)
- In Memoriam: Mark Everingham (Nov 2012)
- Introduction to the Special Section on IEEE Conference on Computer Vision and Pattern Recognition (September 2012)
Call for Papers
- Special Issue on Fine-Grained Visual Categorization
Submission deadline: 30 Sept. 2018
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