76,579 research outputs found
Saliency-guided integration of multiple scans
we present a novel method..
An evaluation method for multiview surface reconstruction algorithms
We propose a new method...
The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain
The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals
Review of the mathematical foundations of data fusion techniques in surface metrology
The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
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