Pose estimation and motion tracking from image sequences is useful in many robotic applications. We propose a real time algorithm which performs these two tasks using an iterative two-stage process: (1) depth prediction and (2) pose calculation. An analytical expression for determining the convergence is also obtained. The algorithm was tested successfully on synthetic and real image sequences.

The appearance of the Function Point technique has allowed the ICT community to increase significantly the practice of software measurement, with respect to the use of the traditional “Lines of Code approach”. A FP count, however, requires a complete and detailed level of descriptive documentation, like the Functional Specifications of the software system under measurement, to be performed. The...

Existing methods for bar code signal reconstruction is based on either the local approach or the regularization approach with total variation penalty. We formulate the problem explicitly in terms of change points of the 0-1 step function. The bar code is then reconstructed by solving the nonlinear least squares problem subject to linear inequality constraints, with starting values provided by t...

Determination of the geometry and topology of a 3-dimensional body is an important problem appearing in Computer Aided Design, medical tomography, crystallography, molecular biology, etc. The methods used to address this problem depend on the form of input data. Let S be a finite set of sample points from a 3-dimensional body K ⊂ R. In applications one may need to estimate certain integral prop...

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. The difficulty is that it is a non-concave maximization problem, and such f...

This paper addresses the issue of estimating the expectation of a real-valued random variable of the form X = g(U) where g is a deterministic function and U can be a random finiteor infinite-dimensional vector. Using recent results on rare event simulation, we propose a unified framework for dealing with both probability and mean estimation for such random variables, i.e. linking algorithms suc...

We study change–point estimation using a two–stage (generalizable to multi–stage) sampling design in a regression setting Y = f(X) + 2. For the talk, the underlying regression function is assumed to be parametrically specified, with a single unknown jump discontinuity of interest. The experimenter is given a budget of n points to make inference on the change–point. The two–stage procedure works...

We consider egomotion estimation in the context of driverassistance systems. In order to estimate the actual vehicle movement we only apply stereo cameras (and not any additional sensor). The paper proposes a visual odometry method by back-mapping clouds of reconstructed 3D points. Our method, called stereo-vision point-cloud back mapping method (sPBM), aims at minimizing 3D back-projection err...

Model reduction via Galerkin projection fails to provide considerable computational savings if applied to general nonlinear systems. This is because the reduced representation of the state vector appears as an argument to the nonlinear function, whose evaluation remains as costly as for the full model. Masked projection approaches, such as the missing point estimation and the (discrete) empiric...

A fast and accurate quantization noise estimator aiming at fixed-point implementations of Digital Signal Processing (DSP) algorithms is presented. The estimator enables significant reduction in the computation time required to perform complex wordlength optimizations. The proposed estimator is based on the use of Affine Arithmetic (AA) and it is presented in two versions: (i) a general version ...