Preface Computer vision is the enterprise of automating and integrating a wide range of processes and representations used for vision perception. It includes many techniques that are useful by themselves, such as image processing (transforming, encoding, and transmitting images) and statistical pattern classification (statistical decision theory applied to general patterns, visual or otherwise)...

In this paper we re-consider the theoretical basis for the Lucas Critique from the point of view of Robust Decision Theory. We ...rst emphasise that the Lucas Critique rests on a weak theoretical paradigm in that it fails to consider the motivation for the policy change by the government and hence inconsistently assumes limited rationality by the government. When placed in a proper dynamic gene...

We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the first part of the dissertation, we propose an approach to approximating the ChapmanKolmogorov equation (CKE) for particle-based nonlinear filtering algorithms, using a new proposal distribution and the improved Fast Gauss Transform (IFGT). The new proposal distribution, used to obtain a Monte Carlo (M...

Visual correspondence represents one of the most important tasks in computer vision. Given two sets of pixels (i.e. two images), it aims at finding corresponding pixel pairs belonging to the two sets (homologous pixels). As a matter of fact, visual correspondence is commonly employed in fields such as stereo correspondence, change detection, image registration, motion estimation, pattern matchi...

Several, apparently incomparable, approaches exist for uncertainty management. Uncertainty management is a broad area applied in many different fields, where information about some underlying, not directly observable, truth–the state of the world–is sought from a set of observations that are more or less reliable. These observations can be, for example, measurements with random and/or systemati...

The most commonly used shape similarity metrics are the sum of squared differences (SSD) and the sum of absolute differences (SAD). However, Maximum Likelihood (ML) theory allows us to relate the noise (differences between feature vectors) distribution more generally to a metric. In this paper, a shape is partitioned into tokens based on its concave regions, invariant moments are computed for e...

This paper presents a summary of the main concepts and references of the Quantitative Feedback Theory (QFT). It is a frequency domain engineering method to design robust controllers. It explicitly emphasises the use of feedback to simultaneously reduce the effects of model plant uncertainty and to satisfy performance specifications. QFT highlights the trade-off (quantification) among the simpli...

Given that a time-delay system is stable for some delay h0 > 0, a procedure is given to find the stability interval [h1, h ∗ 2] such that h0 ∈ [h1, h2] and for all h satisfying h1 < h < h ∗ 2 the system is stable. Further, the system is shown to be unstable if h = h1 or h = h ∗ 2. It is then shown how this can be applied to test the robust stability (with respect to delay values) of a Smith-Pre...

In this paper we survey the primary research, both theoretical and applied, in the field of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results ...

The problem of modeling-uncertainties that may not conform to assumed prior bounds is considered from an adaptive control perspective, but without the standard assumptions of adaptive control. A supervisory control architecture is employed, based on the data-driven logic of unfalsification. The supervisory controller modifies or replaces controllers when sensor data falsifies the hypothesis tha...