193,118 research outputs found
Dynamic Denoising of Tracking Sequences
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.920795In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences.Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other
Self-Reduction Rate of a Microtubule
We formulate and study a quantum field theory of a microtubule, a basic
element of living cells. Following the quantum theory of consciousness by
Hameroff and Penrose, we let the system to reduce to one of the classical
states without measurement if certain conditions are
satisfied(self-reductions), and calculate the self-reduction time (the
mean interval between two successive self-reductions) of a cluster consisting
of more than neighboring tubulins (basic units composing a microtubule).
is interpreted there as an instance of the stream of consciousness. We
analyze the dependence of upon and the initial conditions, etc.
For relatively large electron hopping amplitude, obeys a power law
, which can be explained by the percolation theory. For
sufficiently small values of the electron hopping amplitude, obeys an
exponential law, . By using this law, we estimate the
condition for to take realistic values
\raisebox{-0.5ex}{} sec as \raisebox{-0.5ex}
{} 1000.Comment: 7 pages, 9 figures, Extended versio
Cellular neural networks for motion estimation and obstacle detection
Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN). It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible
Arithmetic Brownian motion subordinated by tempered stable and inverse tempered stable processes
In the last decade the subordinated processes have become popular and found
many practical applications. Therefore in this paper we examine two processes
related to time-changed (subordinated) classical Brownian motion with drift
(called arithmetic Brownian motion). The first one, so called normal tempered
stable, is related to the tempered stable subordinator, while the second one -
to the inverse tempered stable process. We compare the main properties (such as
probability density functions, Laplace transforms, ensemble averaged mean
squared displacements) of such two subordinated processes and propose the
parameters' estimation procedures. Moreover we calibrate the analyzed systems
to real data related to indoor air quality
Statistical modeling of ground motion relations for seismic hazard analysis
We introduce a new approach for ground motion relations (GMR) in the
probabilistic seismic hazard analysis (PSHA), being influenced by the extreme
value theory of mathematical statistics. Therein, we understand a GMR as a
random function. We derive mathematically the principle of area-equivalence;
wherein two alternative GMRs have an equivalent influence on the hazard if
these GMRs have equivalent area functions. This includes local biases. An
interpretation of the difference between these GMRs (an actual and a modeled
one) as a random component leads to a general overestimation of residual
variance and hazard. Beside this, we discuss important aspects of classical
approaches and discover discrepancies with the state of the art of stochastics
and statistics (model selection and significance, test of distribution
assumptions, extreme value statistics). We criticize especially the assumption
of logarithmic normally distributed residuals of maxima like the peak ground
acceleration (PGA). The natural distribution of its individual random component
(equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized
extreme value. We show by numerical researches that the actual distribution can
be hidden and a wrong distribution assumption can influence the PSHA negatively
as the negligence of area equivalence does. Finally, we suggest an estimation
concept for GMRs of PSHA with a regression-free variance estimation of the
individual random component. We demonstrate the advantages of event-specific
GMRs by analyzing data sets from the PEER strong motion database and estimate
event-specific GMRs. Therein, the majority of the best models base on an
anisotropic point source approach. The residual variance of logarithmized PGA
is significantly smaller than in previous models. We validate the estimations
for the event with the largest sample by empirical area functions. etc
A robust motion estimation and segmentation approach to represent moving images with layers
The paper provides a robust representation of moving images based on layers. To that goal, we have designed efficient motion estimation and segmentation techniques by affine model fitting suitable for the construction of layers. Layered representations, originally introduced by Wang and Adelson (see IEEE Transactions on Image Processing, vol.3, no.5, p.625-38, 1994) are important in several applications. In particular they are very appropriate for object tracking, object manipulation and content-based scalability which are among the main functionalities of the future MPEG-4 standard. In addition a variety of examples are provided that give a deep insight into the performance bounds of the representation of moving images using layers.Peer ReviewedPostprint (published version
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