21,805 research outputs found
Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps
In this paper, we address the registration of historical WWII images to
present-day ortho-photo maps for the purpose of geolocalization. Due to the
challenging nature of this problem, we propose to register the images jointly
as a group rather than in a step-by-step manner. To this end, we exploit Hough
Voting spaces as pairwise registration estimators and show how they can be
integrated into a probabilistic groupwise registration framework that can be
efficiently optimized. The feature-based nature of our registration framework
allows to register images with a-priori unknown translational and rotational
relations, and is also able to handle scale changes of up to 30% in our test
data due to a final geometrically guided matching step. The superiority of the
proposed method over existing pairwise and groupwise registration methods is
demonstrated on eight highly challenging sets of historical images with
corresponding ortho-photo maps.Comment: Under review at Elsevier Pattern Recognitio
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse
both low-level and high-level local geometric features for 3D rigid data
matching. It is a common practice to generate distinctive geometric descriptors
by fusing low-level features from various viewpoints or subspaces, or enhance
geometric feature matching by leveraging multiple high-level features. In prior
works, they are typically performed via linear operations such as concatenation
and min pooling. We show that more compact and distinctive representations can
be achieved by optimizing a neural network (NN) model under the triplet
framework that non-linearly fuses local geometric features in Euclidean spaces.
The NN model is trained by an improved triplet loss function that fully
leverages all pairwise relationships within the triplet. Moreover, the fused
descriptor by our approach is also competitive to deep learned descriptors from
raw data while being more lightweight and rotational invariant. Experimental
results on four standard datasets with various data modalities and application
contexts confirm the advantages of our approach in terms of both feature
matching and geometric registration
2D Map Alignment With Region Decomposition
In many applications of autonomous mobile robots the following problem is
encountered. Two maps of the same environment are available, one a prior map
and the other a sensor map built by the robot. To benefit from all available
information in both maps, the robot must find the correct alignment between the
two maps. There exist many approaches to address this challenge, however, most
of the previous methods rely on assumptions such as similar modalities of the
maps, same scale, or existence of an initial guess for the alignment. In this
work we propose a decomposition-based method for 2D spatial map alignment which
does not rely on those assumptions. Our proposed method is validated and
compared with other approaches, including generic data association approaches
and map alignment algorithms. Real world examples of four different
environments with thirty six sensor maps and four layout maps are used for this
analysis. The maps, along with an implementation of the method, are made
publicly available online
The Influence of Intensity Standardization on Medical Image Registration
Acquisition-to-acquisition signal intensity variations (non-standardness) are
inherent in MR images. Standardization is a post processing method for
correcting inter-subject intensity variations through transforming all images
from the given image gray scale into a standard gray scale wherein similar
intensities achieve similar tissue meanings. The lack of a standard image
intensity scale in MRI leads to many difficulties in tissue characterizability,
image display, and analysis, including image segmentation. This phenomenon has
been documented well; however, effects of standardization on medical image
registration have not been studied yet. In this paper, we investigate the
influence of intensity standardization in registration tasks with systematic
and analytic evaluations involving clinical MR images. We conducted nearly
20,000 clinical MR image registration experiments and evaluated the quality of
registrations both quantitatively and qualitatively. The evaluations show that
intensity variations between images degrades the accuracy of registration
performance. The results imply that the accuracy of image registration not only
depends on spatial and geometric similarity but also on the similarity of the
intensity values for the same tissues in different images.Comment: SPIE Medical Imaging 2010 conference paper, and the complete version
of this paper was published in Elsevier Pattern Recognition Letters, volume
31, 201
2D Map Alignment With Region Decomposition
In many applications of autonomous mobile robots the following problem is
encountered. Two maps of the same environment are available, one a prior map
and the other a sensor map built by the robot. To benefit from all available
information in both maps, the robot must find the correct alignment between the
two maps. There exist many approaches to address this challenge, however, most
of the previous methods rely on assumptions such as similar modalities of the
maps, same scale, or existence of an initial guess for the alignment. In this
work we propose a decomposition-based method for 2D spatial map alignment which
does not rely on those assumptions. Our proposed method is validated and
compared with other approaches, including generic data association approaches
and map alignment algorithms. Real world examples of four different
environments with thirty six sensor maps and four layout maps are used for this
analysis. The maps, along with an implementation of the method, are made
publicly available online
Local image registration a comparison for bilateral registration mammography
Early tumor detection is key in reducing the number of breast cancer death
and screening mammography is one of the most widely available and reliable
method for early detection. However, it is difficult for the radiologist to
process with the same attention each case, due the large amount of images to be
read. Computer aided detection (CADe) systems improve tumor detection rate; but
the current efficiency of these systems is not yet adequate and the correct
interpretation of CADe outputs requires expert human intervention. Computer
aided diagnosis systems (CADx) are being designed to improve cancer diagnosis
accuracy, but they have not been efficiently applied in breast cancer. CADx
efficiency can be enhanced by considering the natural mirror symmetry between
the right and left breast. The objective of this work is to evaluate
co-registration algorithms for the accurate alignment of the left to right
breast for CADx enhancement. A set of mammograms were artificially altered to
create a ground truth set to evaluate the registration efficiency of DEMONs,
and SPLINE deformable registration algorithms. The registration accuracy was
evaluated using mean square errors, mutual information and correlation. The
results on the 132 images proved that the SPLINE deformable registration
over-perform the DEMONS on mammography images.Comment: 9 pages, Submitted to The 9th International Seminar on Medical
Information Processing and Analysis (formerly International Seminar on
Medical Image Processing and Analysis) (pending approval
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
We present a new local descriptor for 3D shapes, directly applicable to a
wide range of shape analysis problems such as point correspondences, semantic
segmentation, affordance prediction, and shape-to-scan matching. The descriptor
is produced by a convolutional network that is trained to embed geometrically
and semantically similar points close to one another in descriptor space. The
network processes surface neighborhoods around points on a shape that are
captured at multiple scales by a succession of progressively zoomed out views,
taken from carefully selected camera positions. We leverage two extremely large
sources of data to train our network. First, since our network processes
rendered views in the form of 2D images, we repurpose architectures pre-trained
on massive image datasets. Second, we automatically generate a synthetic dense
point correspondence dataset by non-rigid alignment of corresponding shape
parts in a large collection of segmented 3D models. As a result of these design
choices, our network effectively encodes multi-scale local context and
fine-grained surface detail. Our network can be trained to produce either
category-specific descriptors or more generic descriptors by learning from
multiple shape categories. Once trained, at test time, the network extracts
local descriptors for shapes without requiring any part segmentation as input.
Our method can produce effective local descriptors even for shapes whose
category is unknown or different from the ones used while training. We
demonstrate through several experiments that our learned local descriptors are
more discriminative compared to state of the art alternatives, and are
effective in a variety of shape analysis applications
Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model
The goal of point set registration is to find point-by-point correspondences
between point sets, each of which characterizes the shape of an object. Because
local preservation of object geometry is assumed, prevalent algorithms in the
area can often elegantly solve the problems without using geometric information
specific to the objects. This means that registration performance can be
further improved by using prior knowledge of object geometry. In this paper, we
propose a novel point set registration method using the Gaussian mixture model
with prior shape information encoded as a statistical shape model. Our
transformation model is defined as a combination of the similar transformation,
motion coherence, and the statistical shape model. Therefore, the proposed
method works effectively if the target point set includes outliers and missing
regions, or if it is rotated. The computational cost can be reduced to linear,
and therefore the method is scalable to large point sets. The effectiveness of
the method will be verified through comparisons with existing algorithms using
datasets concerning human body shapes, hands, and faces
Non-Rigid Point Set Registration Networks
Point set registration is defined as a process to determine the spatial
transformation from the source point set to the target one. Existing methods
often iteratively search for the optimal geometric transformation to register a
given pair of point sets, driven by minimizing a predefined alignment loss
function. In contrast, the proposed point registration neural network (PR-Net)
actively learns the registration pattern as a parametric function from a
training dataset, consequently predict the desired geometric transformation to
align a pair of point sets. PR-Net can transfer the learned knowledge (i.e.
registration pattern) from registering training pairs to testing ones without
additional iterative optimization. Specifically, in this paper, we develop
novel techniques to learn shape descriptors from point sets that help formulate
a clear correlation between source and target point sets. With the defined
correlation, PR-Net tends to predict the transformation so that the source and
target point sets can be statistically aligned, which in turn leads to an
optimal spatial geometric registration. PR-Net achieves robust and superior
performance for non-rigid registration of point sets, even in presence of
Gaussian noise, outliers, and missing points, but requires much less time for
registering large number of pairs. More importantly, for a new pair of point
sets, PR-Net is able to directly predict the desired transformation using the
learned model without repetitive iterative optimization routine. Our code is
available at https://github.com/Lingjing324/PR-Net
Performance Evaluation of 3D Correspondence Grouping Algorithms
This paper presents a thorough evaluation of several widely-used 3D
correspondence grouping algorithms, motived by their significance in vision
tasks relying on correct feature correspondences. A good correspondence
grouping algorithm is desired to retrieve as many as inliers from initial
feature matches, giving a rise in both precision and recall. Towards this rule,
we deploy the experiments on three benchmarks respectively addressing shape
retrieval, 3D object recognition and point cloud registration scenarios. The
variety in application context brings a rich category of nuisances including
noise, varying point densities, clutter, occlusion and partial overlaps. It
also results to different ratios of inliers and correspondence distributions
for comprehensive evaluation. Based on the quantitative outcomes, we give a
summarization of the merits/demerits of the evaluated algorithms from both
performance and efficiency perspectives.Comment: Accepted to 3DV 2017, (Spotlight
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