The problem of image registration subsumes a number of problems and techniques in multiframe
image analysis, including the computation of optic flow (general pixel-based motion), stereo
correspondence, structure from motion, and feature tracking. We present a new registration
algorithm based on spline representations of the displacement field which can be specialized to
solve all of the above mentioned problems. In particular, we show how to compute local flow,
global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of
the above problems. Using a spline-based description of the flow removes the need for overlapping
correlation windows, and produces an explicit measure of the correlation between adjacent flow
estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D
projective scene geometry. We also provide results on a number of standard motion sequences.
％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％
This approach addresses two difficulties simultaneously: 1)
the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in
monocular aerial images. With the suggested method building outlines can be detected
faster than the mobile robot can explore the area by itself, giving the robot an ability to
“see” around corners. At the same time, the approach can compensate for the absence
of elevation data in segmentation of aerial images. Our experiments demonstrate that
ground-level semantic information (wall estimates) allows to focus the segmentation of
the aerial image to find buildings and produce a ground-level semantic map that covers
a larger area than can be built using the onboard sensors.
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
GSM (Global System for Mobile communications: originally from Groupe Spécial Mobile) is the most popular standard for mobile phones in the world. Its promoter, the GSM Association, estimates that 80 of the global mobile market uses the standard.[1] GSM is used by over 3 billion people across more than 212 countries and territories.[2][3] Its ubiquity makes international roaming very common between mobile phone operators, enabling subscribers to use their phones in many parts of the world
sba, a C/C++ package for generic sparse bundle adjustment is almost invariably used as the last step of every feature-based multiple view reconstruction vision algorithm to obtain optimal 3D structure and motion (i.e. camera matrix) parameter estimates. Provided with initial estimates, BA simultaneously refines motion and structure by minimizing the reprojection error between the observed and predicted image points.
In this thesis several asp ects of space-time pro cessing and equalization for wire-
less communications are treated. We discuss several di?erent metho ds of improv-
ing estimates of space-time channels, such as temp oral parametrization, spatial
parametrization, reduced rank channel estimation, b o otstrap channel estimation,
and joint estimation of an FIR channel and an AR noise mo del. In wireless commu-
nication the signal is often sub ject to intersymb ol interference as well as interfer-
ence from other users.
