MapSnapper: Engineering an Efficient Algorithm for Matching Images of Maps from Mobile Phones

The MapSnapper project aimed to develop a system for robust matching of low-quality images of a paper map taken from a mobile phone against a high quality digital raster representation of the same map. The paper presents a novel methodology for performing content-based image retrieval and object recognition from query images that have been degraded by noise and subjected to transformations through the imaging system. In addition the paper also provides an insight into the evaluation-driven development process that was used to incrementally improve the matching performance until the design specifications were met

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

A Lens-Calibrated Active Marker Metrology System

This paper presents a prototypical marker tracking system, MT, which is capable of recording multiple mobile robot trajectories in parallel for offline analysis. The system is also capable of providing trajectory data in realtime to agents (such as robots in an arena) and implements several multi-agent operators to simplify agent-based perception. The latter characteristic provides an ability to minimise the normally expensive process of implementing agent-centric perceptual mechanisms and provides a means for multiagent "global knowledge" (Parker 1993)

A sum-of-sinusoids based simulation model for the joint shadowing process in urban peer-to-peer radio channels

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Wireless Broadcast with Network Coding in Mobile Ad-Hoc Networks: DRAGONCAST

Network coding is a recently proposed method for transmitting data, which has been shown to have potential to improve wireless network performance. We study network coding for one specific case of multicast, broadcasting, from one source to all nodes of the network. We use network coding as a loss tolerant, energy-efficient, method for broadcast. Our emphasis is on mobile networks. Our contribution is the proposal of DRAGONCAST, a protocol to perform network coding in such a dynamically evolving environment. It is based on three building blocks: a method to permit real-time decoding of network coding, a method to adjust the network coding transmission rates, and a method for ensuring the termination of the broadcast. The performance and behavior of the method are explored experimentally by simulations; they illustrate the excellent performance of the protocol

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue