University of Adelaide

About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.

Fault Detection Filtering for Nonlinear Switched Stochastic Systems

Abstract: In this note, the fault detection filtering problem is solved for nonlinear switched stochastic system in the T-S fuzzy framework. Our attention is concentrated on the construction of a robust fault detection technique to the nonlinear switched system with Brownian motion. Based on observer-based fault detection fuzzy filter as a residual generator, the proposed fault detection is formulated as a fuzzy filtering problem. By the utilization of the average dwell time technique and the piecewise Lyapunov function technique, the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted ${\mathcal H}_{\infty}$ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is also established by the linearization procedure technique. Finally, simulation has been presented to show the effectiveness of the proposed fault detection technique.

As-Projective-As-Possible Image Stitching with Moving DLT

Abstract: The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”. The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions; the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar. Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user, such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing image stitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimation technique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp to accommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignment that significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thus lessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multiple as-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for large panorama creation.