101 research outputs found
Building models from multiple point sets with kernel density estimation
One of the fundamental problems in computer vision is point set registration. Point
set registration finds use in many important applications and in particular can be considered
one of the crucial stages involved in the reconstruction of models of physical
objects and environments from depth sensor data. The problem of globally aligning
multiple point sets, representing spatial shape measurements from varying sensor viewpoints,
into a common frame of reference is a complex task that is imperative due to
the large number of critical functions that accurate and reliable model reconstructions
contribute to.
In this thesis we focus on improving the quality and feasibility of model and environment
reconstruction through the enhancement of multi-view point set registration
techniques. The thesis makes the following contributions: First, we demonstrate that
employing kernel density estimation to reason about the unknown generating surfaces
that range sensors measure allows us to express measurement variability, uncertainty
and also to separate the problems of model design and viewpoint alignment optimisation.
Our surface estimates define novel view alignment objective functions that inform
the registration process. Our surfaces can be estimated from point clouds in a datadriven
fashion. Through experiments on a variety of datasets we demonstrate that we
have developed a novel and effective solution to the simultaneous multi-view registration
problem.
We then focus on constructing a distributed computation framework capable of solving
generic high-throughput computational problems. We present a novel task-farming
model that we call Semi-Synchronised Task Farming (SSTF), capable of modelling and
subsequently solving computationally distributable problems that benefit from both
independent and dependent distributed components and a level of communication between
process elements. We demonstrate that this framework is a novel schema for
parallel computer vision algorithms and evaluate the performance to establish computational
gains over serial implementations. We couple this framework with an accurate
computation-time prediction model to contribute a novel structure appropriate for
addressing expensive real-world algorithms with substantial parallel performance and
predictable time savings.
Finally, we focus on a timely instance of the multi-view registration problem: modern
range sensors provide large numbers of viewpoint samples that result in an abundance
of depth data information. The ability to utilise this abundance of depth data in a
feasible and principled fashion is of importance to many emerging application areas
making use of spatial information. We develop novel methodology for the registration
of depth measurements acquired from many viewpoints capturing physical object
surfaces. By defining registration and alignment quality metrics based on our density
estimation framework we construct an optimisation methodology that implicitly considers
all viewpoints simultaneously. We use a non-parametric data-driven approach
to consider varying object complexity and guide large view-set spatial transform optimisations.
By aligning large numbers of partial, arbitrary-pose views we evaluate this
strategy quantitatively on large view-set range sensor data where we find that we can
improve registration accuracy over existing methods and contribute increased registration
robustness to the magnitude of coarse seed alignment. This allows large-scale
registration on problem instances exhibiting varying object complexity with the added
advantage of massive parallel efficiency
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image slices from 3D volumes, with respect to a learned canonical
atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks
(CNNs) to learn the highly complex regression function that maps 2D image
slices into their correct position and orientation in 3D space. Our approach is
attractive in challenging imaging scenarios, where significant subject motion
complicates reconstruction performance of 3D volumes from 2D slice data. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated MRI brain data with extreme random motion. We further demonstrate
qualitative results on fetal MRI where our method is integrated into a full
reconstruction and motion compensation pipeline. With our CNN regression
approach we obtain an average prediction error of 7mm on simulated data, and
convincing reconstruction quality of images of very young fetuses where
previous methods fail. We further discuss applications to Computed Tomography
and X-ray projections. Our approach is a general solution to the 2D/3D
initialization problem. It is computationally efficient, with prediction times
per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under
review for MICCAI 201
- …