2,357 research outputs found
Automatic image registration and defect identification of a class of structural artifacts in printed documents
The work in this thesis proposes a defect analysis system, which automatically aligns a digitized copy of a printed output to a reference electronic original and highlights image defects. We focus on a class of image defects or artifacts caused by shortfalls in the mechanical or electro-photographic processes that include spots, deletions and debris missing deletions. The algorithm begins with image registration performed using a logpolar transformation and mutual information techniques. A confidence map is then calculated by comparing the contrast and entropy in the neighborhood of each pixel in both the printed document and corresponding electronic original. This results in a qualitative difference map of the two images highlighting the detected defects. The algorithm was demonstrated successfully on a collection of 99 printed images based on 11 original electronic images and test patterns printed on 9 different faulty printers provided by Xerox Corporation. The proposed algorithm is effective in aligning digitized printed output irrespective of translation, rotation and scale variations, and identifying defects in color inconsistent hardcopies
An efficient stochastic approach to groupwise non-rigid image registration
The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, in- cluding medical imaging, automatic construction of statis- tical models of appearance and analysis of facial dynam- ics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of ex- amples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed suffi- ciently in the groupwise registration literature. In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel di- mensionality reduction techniques specific to the problem of groupwise image registration and from comparative insen- sitivity of the adopted optimisation scheme (Simultaneous Perturbation Stochastic Approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algo- rithm is formulated in way readily suited to implementation on graphics processing units (GPU). In evaluation of our method we show a high robust- ness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter- and intra-person variation, and show consid- erable improvement in terms of accuracy of solution and speed compared to traditional methods
Multimodality and Nonrigid Image Registration with Application to Diffusion Tensor Imaging
The great challenge in image registration is to devise computationally efficient algorithms for aligning images
so that their details overlap accurately. The first problem addressed in this thesis is multimodality
medical image registration, which we formulate as an optimization problem in the information-theoretic setting.
We introduce a viable and practical image registration method by maximizing a generalized entropic
dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The
feasibility of the proposed image registration approach is demonstrated through extensive experiments.
The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic
framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton
method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between
the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy
and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in
such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration
accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance
data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein,
we extend our nonrigid registration approach to align diffusion tensor images for multiple components by
enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration
algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image
registration indicate the feasibility of the proposed approach and a much better performance compared to
the affine registration method based on mutual information, not only in terms of registration accuracy in the
presence of geometric distortions but also in terms of robustness in the presence of Rician noise
Frame registration for motion compensation in imaging photoplethysmography
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Imaging photoplethysmography (iPPG) is an emerging technology used to assess microcirculation and cardiovascular signs by collecting backscattered light from illuminated tissue using optical imaging sensors. An engineering approach is used to evaluate whether a silicone cast of a human palm might be effectively utilized to predict the results of image registration schemes for motion compensation prior to their application on live human tissue. This allows us to establish a performance baseline for each of the algorithms and to isolate performance and noise fluctuations due to the induced motion from the temporally changing physiological signs. A multi-stage evaluation model is developed to qualitatively assess the influence of the region of interest (ROI), system resolution and distance, reference frame selection, and signal normalization on extracted iPPG waveforms from live tissue. We conclude that the application of image registration is able to deliver up to 75% signal-to-noise (SNR) improvement (4.75 to 8.34) over an uncompensated iPPG signal by employing an intensity-based algorithm with a moving reference frame
The Automatic Neuroscientist: automated experimental design with real-time fMRI
A standard approach in functional neuroimaging explores how a particular
cognitive task activates a set of brain regions (one task-to-many regions
mapping). Importantly though, the same neural system can be activated by
inherently different tasks. To date, there is no approach available that
systematically explores whether and how distinct tasks probe the same neural
system (many tasks-to-region mapping). In our work, presented here we propose
an alternative framework, the Automatic Neuroscientist, which turns the typical
fMRI approach on its head. We use real-time fMRI in combination with
state-of-the-art optimisation techniques to automatically design the optimal
experiment to evoke a desired target brain state. Here, we present two
proof-of-principle studies involving visual and auditory stimuli. The data
demonstrate this closed-loop approach to be very powerful, hugely speeding up
fMRI and providing an accurate estimation of the underlying relationship
between stimuli and neural responses across an extensive experimental parameter
space. Finally, we detail four scenarios where our approach can be applied,
suggesting how it provides a novel description of how cognition and the brain
interrelate.Comment: 22 pages, 7 figures, work presented at OHBM 201
High-level environment representations for mobile robots
In most robotic applications we are faced with the problem of building
a digital representation of the environment that allows the robot to
autonomously complete its tasks. This internal representation can be
used by the robot to plan a motion trajectory for its mobile base
and/or end-effector. For most man-made environments we do not have
a digital representation or it is inaccurate. Thus, the robot must
have the capability of building it autonomously. This is done by
integrating into an internal data structure incoming sensor
measurements. For this purpose, a common solution consists in solving
the Simultaneous Localization and Mapping (SLAM) problem. The map
obtained by solving a SLAM problem is called ``metric'' and it
describes the geometric structure of the environment. A metric map is
typically made up of low-level primitives (like points or
voxels). This means that even though it represents the shape of the
objects in the robot workspace it lacks the information of which
object a surface belongs to. Having an object-level representation of
the environment has the advantage of augmenting the set of possible
tasks that a robot may accomplish. To this end, in this thesis we
focus on two aspects. We propose a formalism to represent in a uniform
manner 3D scenes consisting of different geometric primitives,
including points, lines and planes. Consequently, we derive a local
registration and a global optimization algorithm that can exploit this
representation for robust estimation. Furthermore, we present a
Semantic Mapping system capable of building an \textit{object-based}
map that can be used for complex task planning and execution. Our
system exploits effective reconstruction and recognition techniques
that require no a-priori information about the environment and can be
used under general conditions
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