Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics, most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainly to accompany observations of the environment. This paper describes how uncertainly can be characterised for a vision system that locates coloured landmark in a typical laboratory environment. The paper describes a model of the uncertainly in segmentation, the internal camera model and the mounting of the camera on the robot. It =plains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainly model,

Multiple camera management using wide baseline matching

Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera networks, cameras are placed far apart to maximise coverage. This is referred to as a wide base-line scenario. Finding sufficient correspondences for camera calibration in wide base-line scenarios presents a significant challenge. This thesis focuses on developing more effective and efficient techniques for finding correspondences in uncalibrated, wide baseline, multiple-camera scenarios. The project consists of two major areas of work. The first is the development of more effective and efficient view covariant local feature extractors. The second area involves finding methods to extract scene information using the information contained in a limited set of matched affine features. Several novel affine adaptation techniques for salient features have been developed. A method is presented for efficiently computing the discrete scale space primal sketch of local image features. A scale selection method was implemented that makes use of the primal sketch. The primal sketch-based scale selection method has several advantages over the existing methods. It allows greater freedom in how the scale space is sampled, enables more accurate scale selection, is more effective at combining different functions for spatial position and scale selection, and leads to greater computational efficiency. Existing affine adaptation methods make use of the second moment matrix to estimate the local affine shape of local image features. In this thesis, it is shown that the Hessian matrix can be used in a similar way to estimate local feature shape. The Hessian matrix is effective for estimating the shape of blob-like structures, but is less effective for corner structures. It is simpler to compute than the second moment matrix, leading to a significant reduction in computational cost. A wide baseline dense correspondence extraction system, called WiDense, is presented in this thesis. It allows the extraction of large numbers of additional accurate correspondences, given only a few initial putative correspondences. It consists of the following algorithms: An affine region alignment algorithm that ensures accurate alignment between matched features; A method for extracting more matches in the vicinity of a matched pair of affine features, using the alignment information contained in the match; An algorithm for extracting large numbers of highly accurate point correspondences from an aligned pair of feature regions. Experiments show that the correspondences generated by the WiDense system improves the success rate of computing the epipolar geometry of very widely separated views. This new method is successful in many cases where the features produced by the best wide baseline matching algorithms are insufficient for computing the scene geometry.

BLAST Atlas : a function based multiple genome browser

BLAST Atlas is a visual analysis system for comparative genomics that supports genome-wide gene characterisation, functional assignment and function-based browsing of one or more chromosomes. Inspired by applications such as the WorldWide Telescope, Bing Maps 3D and Google Earth, BLAST Atlas uses novel three-dimensional gene and function views that provide a highly interactive and intuitive way for scientists to navigate, query and compare gene annotations. The system can be used for gene identification and functional assignment or as a function-based multiple genome comparison tool which complements existing position based comparison and alignment viewers.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Robust speaker verification via fusion of speech and lip modalities

This paper investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. It has been previously shown in our own work, and in the work of others, that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms the performance of either sub-system. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise

Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountain biking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected.

Useful field of view predicts driving in the presence of distracters

Purpose. The Useful Field of View (UFOV(R)) test has been shown to be highly effective in predicting crash risk among older adults. An important question which we examined in this study is whether this association is due to the ability of the UFOV to predict difficulties in attention-demanding driving situations that involve either visual or auditory distracters. Methods. Participants included 92 community-living adults (mean age 73.6 +/- 5.4 years; range 65-88 years) who completed all three subtests of the UFOV involving assessment of visual processing speed (subtest 1), divided attention (subtest 2), and selective attention (subtest 3); driving safety risk was also classified using the UFOV scoring system. Driving performance was assessed separately on a closed-road circuit while driving under three conditions: no distracters, visual distracters, and auditory distracters. Driving outcome measures included road sign recognition, hazard detection, gap perception, time to complete the course, and performance on the distracter tasks. Results. Those rated as safe on the UFOV (safety rating categories 1 and 2), as well as those responding faster than the recommended cut-off on the selective attention subtest (350 msec), performed significantly better in terms of overall driving performance and also experienced less interference from distracters. Of the three UFOV subtests, the selective attention subtest best predicted overall driving performance in the presence of distracters. Conclusions. Older adults who were rated as higher risk on the UFOV, particularly on the selective attention subtest, demonstrated poorest driving performance in the presence of distracters. This finding suggests that the selective attention subtest of the UFOV may be differentially more effective in predicting driving difficulties in situations of divided attention which are commonly associated with crashes.

Information sharing in Onesocialweb using a multiple profile manager to better informed medical decision making processes

The study shows an alternative solution to existing efforts at solving the problem of how to centrally manage and synchronise users’ Multiple Profiles (MP) across multiple discrete social networks. Most social network users hold more than one social network account and utilise them in different ways depending on the digital context (Iannella, 2009a). They may, for example, enjoy friendly chat on Facebook1, professional discussion on LinkedIn2, and health information exchange on PatientsLikeMe3 In this thesis the researcher proposes a framework for the management of a user’s multiple online social network profiles. A demonstrator, called Multiple Profile Manager (MPM), will be showcased to illustrate how effective the framework will be. The MPM will achieve the required profile management and synchronisation using a free, open, decentralized social networking platform (OSW) that was proposed by the Vodafone Group in 2010. The proposed MPM will enable a user to create and manage an integrated profile (IP) and share/synchronise this profile with all their social networks. The necessary protocols to support the prototype are also proposed by the researcher. The MPM protocol specification defines an Extensible Messaging and Presence Protocol (XMPP) extension for sharing vCard and social network accounts information between the MPM Server, MPM Client, and social network sites (SNSs). . Therefore many web users need to manage disparate profiles across many distributed online sources. Maintaining these profiles is cumbersome, time-consuming, inefficient, and may lead to lost opportunity. The writer of this thesis adopted a research approach and a number of use cases for the implementation of the project. The use cases were created to capture the functional requirements of the MPM and to describe the interactions between users and the MPM. In the research a development process was followed in establishing the prototype and related protocols. The use cases were subsequently used to illustrate the prototype via the screenshots taken of the MPM client interfaces. The use cases also played a role in evaluating the outcomes of the research such as the framework, prototype, and the related protocols. An innovative application of this project is in the area of public health informatics. The researcher utilised the prototype to examine how the framework might benefit patients and physicians. The framework can greatly enhance health information management for patients and more importantly offer a more comprehensive personal health overview of patients to physicians. This will give a more complete picture of the patient’s background than is currently available and will prove helpful in providing the right treatment. The MPM prototype and related protocols have a high application value as they can be integrated into the real OSW platform and so serve users in the modern digital world. They also provide online users with a real platform for centrally storing their complete profile data, efficiently managing their personal information, and moreover, synchronising the overall complete profile with each of their discrete profiles stored in their different social network sites.

Image-based visual servoing for the super-orbital re-entry of Hayabusa spacecraft

This paper presents an image-based visual servoing system that was used to track the atmospheric Earth re-entry of Hayabusa. The primary aim of this ground based tracking platform was to record the emission spectrum radiating from the superheated gas of the shock layer and the surface of the heat shield during re-entry. To the author's knowledge, this is the first time that a visual servoing system has successfully tracked a super-orbital re-entry of a spacecraft and recorded its pectral signature. Furthermore, we improved the system by including a simplified dynamic model for feed-forward control and demonstrate improved tracking performance on the International Space Station (ISS). We present comparisons between simulation and experimental results on different target trajectories including tracking results from Hayabusa and ISS. The required performance for tracking both spacecraft is demanding when combined with a narrow field of view (FOV). We also briefly discuss the preliminary results obtained from the spectroscopy of the Hayabusa's heat shield during re-entry.

Online multiple profile manager for eHealth information sharing

Online social networks connect millions of people around the globe. These electronic bonds make individuals comfortable with their behaviours. Such positive signs of sharing information is useful phenomena requires consideration to establish a socio-scientific effect. Recently, many web users have more than one social networking account. This means a user may hold multiple profiles which are stored in different Social Network Sites (SNNs). Maintaining these multiple online social network profiles is cumbersome and time-consuming [1]. In this paper we will propose a framework for the management of a user's multiple profiles. A demonstrator, called Multiple Profile Manager (MPM), will be showcased to illustrate how effective the framework will be.

Vision-based place recognition : how low can you go?

In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.