16,689 research outputs found
Spatial transformations of diffusion tensor magnetic resonance images
The authors address the problem of applying spatial transformations (or “image warps”) to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. The authors present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how their methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of the authors' methods improve the consistency between registered and target images over naive warping algorithms
Robust point correspondence applied to two and three-dimensional image registration
Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (correspondence by sensitivity to movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondence
Group Invariant Deep Representations for Image Instance Retrieval
Most image instance retrieval pipelines are based on comparison of vectors
known as global image descriptors between a query image and the database
images. Due to their success in large scale image classification,
representations extracted from Convolutional Neural Networks (CNN) are quickly
gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors
for image instance retrieval. While CNN-based descriptors are generally
remarked for good retrieval performance at lower bitrates, they nevertheless
present a number of drawbacks including the lack of robustness to common object
transformations such as rotations compared with their interest point based FV
counterparts.
In this paper, we propose a method for computing invariant global descriptors
from CNNs. Our method implements a recently proposed mathematical theory for
invariance in a sensory cortex modeled as a feedforward neural network. The
resulting global descriptors can be made invariant to multiple arbitrary
transformation groups while retaining good discriminativeness.
Based on a thorough empirical evaluation using several publicly available
datasets, we show that our method is able to significantly and consistently
improve retrieval results every time a new type of invariance is incorporated.
We also show that our method which has few parameters is not prone to
overfitting: improvements generalize well across datasets with different
properties with regard to invariances. Finally, we show that our descriptors
are able to compare favourably to other state-of-the-art compact descriptors in
similar bitranges, exceeding the highest retrieval results reported in the
literature on some datasets. A dedicated dimensionality reduction step
--quantization or hashing-- may be able to further improve the competitiveness
of the descriptors
Disorder-Driven Pretransitional Tweed in Martensitic Transformations
Defying the conventional wisdom regarding first--order transitions, {\it
solid--solid displacive transformations} are often accompanied by pronounced
pretransitional phenomena. Generally, these phenomena are indicative of some
mesoscopic lattice deformation that ``anticipates'' the upcoming phase
transition. Among these precursive effects is the observation of the so-called
``tweed'' pattern in transmission electron microscopy in a wide variety of
materials. We have investigated the tweed deformation in a two dimensional
model system, and found that it arises because the compositional disorder
intrinsic to any alloy conspires with the natural geometric constraints of the
lattice to produce a frustrated, glassy phase. The predicted phase diagram and
glassy behavior have been verified by numerical simulations, and diffraction
patterns of simulated systems are found to compare well with experimental data.
Analytically comparing to alternative models of strain-disorder coupling, we
show that the present model best accounts for experimental observations.Comment: 43 pages in TeX, plus figures. Most figures supplied separately in
uuencoded format. Three other figures available via anonymous ftp
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
Gauge Invariant Framework for Shape Analysis of Surfaces
This paper describes a novel framework for computing geodesic paths in shape
spaces of spherical surfaces under an elastic Riemannian metric. The novelty
lies in defining this Riemannian metric directly on the quotient (shape) space,
rather than inheriting it from pre-shape space, and using it to formulate a
path energy that measures only the normal components of velocities along the
path. In other words, this paper defines and solves for geodesics directly on
the shape space and avoids complications resulting from the quotient operation.
This comprehensive framework is invariant to arbitrary parameterizations of
surfaces along paths, a phenomenon termed as gauge invariance. Additionally,
this paper makes a link between different elastic metrics used in the computer
science literature on one hand, and the mathematical literature on the other
hand, and provides a geometrical interpretation of the terms involved. Examples
using real and simulated 3D objects are provided to help illustrate the main
ideas.Comment: 15 pages, 11 Figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence in a better resolutio
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