18,593 research outputs found
Gaussian Process Morphable Models
Statistical shape models (SSMs) represent a class of shapes as a normal
distribution of point variations, whose parameters are estimated from example
shapes. Principal component analysis (PCA) is applied to obtain a
low-dimensional representation of the shape variation in terms of the leading
principal components. In this paper, we propose a generalization of SSMs,
called Gaussian Process Morphable Models (GPMMs). We model the shape variations
with a Gaussian process, which we represent using the leading components of its
Karhunen-Loeve expansion. To compute the expansion, we make use of an
approximation scheme based on the Nystrom method. The resulting model can be
seen as a continuous analogon of an SSM. However, while for SSMs the shape
variation is restricted to the span of the example data, with GPMMs we can
define the shape variation using any Gaussian process. For example, we can
build shape models that correspond to classical spline models, and thus do not
require any example data. Furthermore, Gaussian processes make it possible to
combine different models. For example, an SSM can be extended with a spline
model, to obtain a model that incorporates learned shape characteristics, but
is flexible enough to explain shapes that cannot be represented by the SSM. We
introduce a simple algorithm for fitting a GPMM to a surface or image. This
results in a non-rigid registration approach, whose regularization properties
are defined by a GPMM. We show how we can obtain different registration
schemes,including methods for multi-scale, spatially-varying or hybrid
registration, by constructing an appropriate GPMM. As our approach strictly
separates modelling from the fitting process, this is all achieved without
changes to the fitting algorithm. We show the applicability and versatility of
GPMMs on a clinical use case, where the goal is the model-based segmentation of
3D forearm images
Extracting 3D parametric curves from 2D images of Helical objects
Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively
A Flexible Modeling Approach for Robust Multi-Lane Road Estimation
A robust estimation of road course and traffic lanes is an essential part of
environment perception for next generations of Advanced Driver Assistance
Systems and development of self-driving vehicles. In this paper, a flexible
method for modeling multiple lanes in a vehicle in real time is presented.
Information about traffic lanes, derived by cameras and other environmental
sensors, that is represented as features, serves as input for an iterative
expectation-maximization method to estimate a lane model. The generic and
modular concept of the approach allows to freely choose the mathematical
functions for the geometrical description of lanes. In addition to the current
measurement data, the previously estimated result as well as additional
constraints to reflect parallelism and continuity of traffic lanes, are
considered in the optimization process. As evaluation of the lane estimation
method, its performance is showcased using cubic splines for the geometric
representation of lanes in simulated scenarios and measurements recorded using
a development vehicle. In a comparison to ground truth data, robustness and
precision of the lanes estimated up to a distance of 120 m are demonstrated. As
a part of the environmental modeling, the presented method can be utilized for
longitudinal and lateral control of autonomous vehicles
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