90,753 research outputs found
Using Topological Statistics to Detect Determinism in Time Series
Statistical differentiability of the measure along the reconstructed
trajectory is a good candidate to quantify determinism in time series. The
procedure is based upon a formula that explicitly shows the sensitivity of the
measure to stochasticity. Numerical results for partially surrogated time
series and series derived from several stochastic models, illustrate the
usefulness of the method proposed here. The method is shown to work also for
high--dimensional systems and experimental time seriesComment: 23 RevTeX pages, 14 eps figures. To appear in Physical Review
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
Bayesian matching of unlabeled marked point sets using random fields, with an application to molecular alignment
Statistical methodology is proposed for comparing unlabeled marked point
sets, with an application to aligning steroid molecules in chemoinformatics.
Methods from statistical shape analysis are combined with techniques for
predicting random fields in spatial statistics in order to define a suitable
measure of similarity between two marked point sets. Bayesian modeling of the
predicted field overlap between pairs of point sets is proposed, and posterior
inference of the alignment is carried out using Markov chain Monte Carlo
simulation. By representing the fields in reproducing kernel Hilbert spaces,
the degree of overlap can be computed without expensive numerical integration.
Superimposing entire fields rather than the configuration matrices of point
coordinates thereby avoids the problem that there is usually no clear
one-to-one correspondence between the points. In addition, mask parameters are
introduced in the model, so that partial matching of the marked point sets can
be carried out. We also propose an adaptation of the generalized Procrustes
analysis algorithm for the simultaneous alignment of multiple point sets. The
methodology is illustrated with a simulation study and then applied to a data
set of 31 steroid molecules, where the relationship between shape and binding
activity to the corticosteroid binding globulin receptor is explored.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS486 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Reference face graph for face recognition
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation
Transport in time-dependent dynamical systems: Finite-time coherent sets
We study the transport properties of nonautonomous chaotic dynamical systems
over a finite time duration. We are particularly interested in those regions
that remain coherent and relatively non-dispersive over finite periods of time,
despite the chaotic nature of the system. We develop a novel probabilistic
methodology based upon transfer operators that automatically detects maximally
coherent sets. The approach is very simple to implement, requiring only
singular vector computations of a matrix of transitions induced by the
dynamics. We illustrate our new methodology on an idealized stratospheric flow
and in two and three dimensional analyses of European Centre for Medium Range
Weather Forecasting (ECMWF) reanalysis data
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