Cross-calbration of push-broom 2D LIDARs and cameras in natural scenes

This paper addresses the problem of automatically estimating the relative pose between a push-broom LIDAR and a camera without the need for artificial calibration targets or other human intervention. Further we do not require the sensors to have an overlapping field of view, it is enough that they observe the same scene but at different times from a moving platform. Matching between sensor modalities is achieved without feature extraction. We present results from field trials which suggest that this new approach achieves an extrinsic calibration accuracy of millimeters in translation and deci-degrees in rotation.

Exact and approximate Bayesian inference for low count time series models with intractable likelihoods

In this paper we present a new simulation methodology in order to obtain exact or approximate Bayesian inference for models for low-valued count time series data that have computationally demanding likelihood functions. The algorithm fits within the framework of particle Markov chain Monte Carlo (PMCMC) methods. The particle filter requires only model simulations and, in this regard, our approach has connections with approximate Bayesian computation (ABC). However, an advantage of using the PMCMC approach in this setting is that simulated data can be matched with data observed one-at-a-time, rather than attempting to match on the full dataset simultaneously or on a low-dimensional non-sufficient summary statistic, which is common practice in ABC. For low-valued count time series data we find that it is often computationally feasible to match simulated data with observed data exactly. Our particle filter maintains $N$ particles by repeating the simulation until $N+1$ exact matches are obtained. Our algorithm creates an unbiased estimate of the likelihood, resulting in exact posterior inferences when included in an MCMC algorithm. In cases where exact matching is computationally prohibitive, a tolerance is introduced as per ABC. A novel aspect of our approach is that we introduce auxiliary variables into our particle filter so that partially observed and/or non-Markovian models can be accommodated. We demonstrate that Bayesian model choice problems can be easily handled in this framework.

Long exposure localization in darkness using consumer cameras

In this paper we demonstrate passive vision-based localization in environments more than two orders of magnitude darker than the current benchmark using a 100 webcam and a 500 camera. Our approach uses the camera’s maximum exposure duration and sensor gain to achieve appropriately exposed images even in unlit night-time environments, albeit with extreme levels of motion blur. Using the SeqSLAM algorithm, we first evaluate the effect of variable motion blur caused by simulated exposures of 132 ms to 10000 ms duration on localization performance. We then use actual long exposure camera datasets to demonstrate day-night localization in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed greyscale images to using patch normalization and local neighbourhood normalization – the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function across extreme perceptual change.

The great divide : bridging the gap

Learning Objective: To describe a collaborative system of clinical allocations using a dedicated, discipline specific administrative coordinator. Methods: The Clinical Placement Coordinator is the liaison person between the student, the academic staff and the clinical sites, and fills an important role in bridging the gap to enhance the student learning experience. With this in mind the Coordinator is very discipline focused and works closely with the academic staff who coordinate the clinical units within the program. This person is the ‘‘face’’ of QUT to the external stakeholders, and ensures that all parties experience a smooth process. This no mean feat given that there are over 350 students to be placed annually, across 14 separate clinical blocks ranging from 1 to 6 weeks in length at various sites. The processes involved in clinical placement allocation will be presented, and the roles of the staff in facilitating students’ placement preferences and matching with clinical site offers will be described. In many allied health programs in Australia, the clinical placement activity is carried out by an academic member of staff. However, this can result in delays in communications due to other workload requirements such as lecture, tutorial and practical class commitments. Having a dedicated knowledgeable administration officer has resulted in a person being available to take calls from clinical staff, meet with students to discuss allocation needs and ensure that academic staff are consulted if and when necessary. The Clinical Placement Coordinator is very much a part of the course team and attends professional meetings and conferences as an avenue of networking and meeting clinical staff. Results: The success in having a dedicated administrative officer as the Clinical Placement Coordinator acting as the conduit between academic staff and students, and the university and clinical staff has been highly successful to date. This was noted in commendations from the 2010 Course Accreditation Panel Report which stated: ‘‘The very positive perception in the professional community of Ms Margaret McBurney’s effective and efficient organization of student clinical placements. Students and clinical professionals commented favourably on the approachability of staff. There is confidence that program staff will follow up on issues raised urgently in clinical centres.’’

Online calibration of stereo rigs for long-term autonomy

Stereo-based visual odometry algorithms are heavily dependent on an accurate calibration of the rigidly fixed stereo pair. Even small shifts in the rigid transform between the cameras can impact on feature matching and 3D scene triangulation, adversely affecting pose estimates and applications dependent on long-term autonomy. In many field-based scenarios where vibration, knocks and pressure change affect a robotic vehicle, maintaining an accurate stereo calibration cannot be guaranteed over long periods. This paper presents a novel method of recalibrating overlapping stereo camera rigs from online visual data while simultaneously providing an up-to-date and up-to-scale pose estimate. The proposed technique implements a novel form of partitioned bundle adjustment that explicitly includes the homogeneous transform between a stereo camera pair to generate an optimal calibration. Pose estimates are computed in parallel to the calibration, providing online recalibration which seamlessly integrates into a stereo visual odometry framework. We present results demonstrating accurate performance of the algorithm on both simulated scenarios and real data gathered from a wide-baseline stereo pair on a ground vehicle traversing urban roads.

Improving statistical analysis of matched case-control studies

Matched case–control research designs can be useful because matching can increase power due to reduced variability between subjects. However, inappropriate statistical analysis of matched data could result in a change in the strength of association between the dependent and independent variables or a change in the significance of the findings. We sought to ascertain whether matched case–control studies published in the nursing literature utilized appropriate statistical analyses. Of 41 articles identified that met the inclusion criteria, 31 (76%) used an inappropriate statistical test for comparing data derived from case subjects and their matched controls. In response to this finding, we developed an algorithm to support decision-making regarding statistical tests for matched case–control studies.

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.

Digital social signal processing - theorectical underpinning and research agenda

Organizations make increasingly use of social media in order to compete for customer awareness and improve the quality of their goods and services. Multiple techniques of social media analysis are already in use. Nevertheless, theoretical underpinnings and a sound research agenda are still unavailable in this field at the present time. In order to contribute to setting up such an agenda, we introduce digital social signal processing (DSSP) as a new research stream in IS that requires multi-facetted investigations. Our DSSP concept is founded upon a set of four sequential activities: sensing digital social signals that are emitted by individuals on social media; decoding online data of social media in order to reconstruct digital social signals; matching the signals with consumers’ life events; and configuring individualized goods and service offerings tailored to the individual needs of customers. We further contribute to tying loose ends of different research areas together, in order to frame DSSP as a field for further investigation. We conclude with developing a research agenda.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

NTCIR-10 crosslink-2 task : a link mining strategy

At NTCIR-10 we participated in the cross-lingual link discovery (CrossLink-2) task. In this paper we describe our systems for discovering cross-lingual links between the Chinese, Japanese, and Korean (CJK) Wikipedia and the English Wikipedia. The evaluation results show that our implementation of the cross-lingual linking method achieved promising results.

Count-min sketches for estimating password frequency within Hamming distance two

The count-min sketch is a useful data structure for recording and estimating the frequency of string occurrences, such as passwords, in sub-linear space with high accuracy. However, it cannot be used to draw conclusions on groups of strings that are similar, for example close in Hamming distance. This paper introduces a variant of the count-min sketch which allows for estimating counts within a specified Hamming distance of the queried string. This variant can be used to prevent users from choosing popular passwords, like the original sketch, but it also allows for a more efficient method of analysing password statistics.

Accurate pattern prediction of very closely spaced mismatched antennas using measured active element patterns

A method for prediction of the radiation pattern of N strongly coupled antennas with mismatched sources is presented. The method facilitates fast and accurate design of compact arrays. The prediction is based on the measured N-port S parameters of the coupled antennas and the N active element patterns measured in a 50 ω environment. By introducing equivalent power sources, the radiation pattern with excitation by sources with arbitrary impedances and various decoupling and matching networks (DMN) can be accurately predicted without the need for additional measurements. Two experiments were carried out for verification: pattern prediction for parasitic antennas with different loads and for antennas with DMN. The difference between measured and predicted patterns was within 1 to 2 dB.

Person re-identification using group information

After first observing a person, the task of person re-identification involves recognising an individual at different locations across a network of cameras at a later time. Traditionally, this task has been performed by first extracting appearance features of an individual and then matching these features to the previous observation. However, identifying an individual based solely on appearance can be ambiguous, particularly when people wear similar clothing (i.e. people dressed in uniforms in sporting and school settings). This task is made more difficult when the resolution of the input image is small as is typically the case in multi-camera networks. To circumvent these issues, we need to use other contextual cues. In this paper, we use "group" information as our contextual feature to aid in the re-identification of a person, which is heavily motivated by the fact that people generally move together as a collective group. To encode group context, we learn a linear mapping function to assign each person to a "role" or position within the group structure. We then combine the appearance and group context cues using a weighted summation. We demonstrate how this improves performance of person re-identification in a sports environment over appearance based-features.

Education higher degree research students writing for publication

Higher Degree Research (HDR) student publications are increasingly valued by students, by professional communities and by research institutions. Peer-reviewed publications form the HDR student writer's publication track record and increase competitiveness in employment and research funding opportunities. These publications also make the results of HDR student research available to the community in accessible formats. HDR student publications are also valued by universities because they provide evidence of institutional research activity within a field and attract a return on research performance. However, although publications are important to multiple stakeholders, many Education HDR students do not publish the results of their research. Hence, an investigation of Education HDR graduates who submitted work for publication during their candidacy was undertaken. This multiple, explanatory case study investigated six recent Education HDR graduates who had submitted work to peer-reviewed outlets during their candidacy. The conceptual framework supported an analysis of the development of Education HDR student writing using Alexander's (2003, 2004) Model of Domain Learning which focuses on expertise, and Lave and Wenger's (1991) situated learning within a community of practice. Within this framework, the study investigated how these graduates were able to submit or publish their research despite their relative lack of writing expertise. Case data were gathered through interviews and from graduate publication records. Contextual data were collected through graduate interviews, from Faculty and university documents, and through interviews with two Education HDR supervisors. Directed content analysis was applied to all data to ascertain the support available in the research training environment. Thematic analysis of graduate and supervisor interviews was then undertaken to reveal further information on training opportunities accessed by the HDR graduates. Pattern matching of all interview transcripts provided information on how the HDR graduates developed writing expertise. Finally, explanation building was used to determine causal links between the training accessed by the graduates and their writing expertise. The results demonstrated that Education HDR graduates developed publications and some level of expertise simultaneously within communities of practice. Students were largely supported by supervisors who played a critical role. They facilitated communities of practice and largely mediated HDR engagement in other training opportunities. However, supervisor support alone did not ensure that the HDR graduates developed writing expertise. Graduates who appeared to develop the most expertise, and produce a number of publications reported experiencing both a sustained period of engagement within one community of practice, and participation in multiple communities of practice. The implications for the MDL theory, as applied to academic writing, suggests that communities of practice can assist learners to progress from initial contact with a new domain of interest through to competence. The implications for research training include the suggestion that supervisors as potentially crucial supporters of HDR student writing for publication should themselves be active publishers. Also, Faculty or university sponsorship of communities of practice focussed on HDR student writing for publication could provide effective support for the development of HDR student writing expertise and potentially increase the number of their peer-reviewed publications.

Electrospinning and crosslinking of low-molecular-weight poly(trimethylene carbonate-co-L-lactide) as an elastomeric scaffold for vascular engineering

The growth of suitable tissue to replace natural blood vessels requires a degradable scaffold material that is processable into porous structures with appropriate mechanical and cell growth properties. This study investigates the fabrication of degradable, crosslinkable prepolymers of l-lactide-co-trimethylene carbonate into porous scaffolds by electrospinning. After crosslinking by γ-radiation, dimensionally stable scaffolds were obtained with up to 56% trimethylene carbonate incorporation. The fibrous mats showed Young’s moduli closely matching human arteries (0.4–0.8 MPa). Repeated cyclic extension yielded negligible change in mechanical properties, demonstrating the potential for use under dynamic physiological conditions. The scaffolds remained elastic and resilient at 30% strain after 84 days of degradation in phosphate buffer, while the modulus and ultimate stress and strain progressively decreased. The electrospun mats are mechanically superior to solid films of the same materials. In vitro, human mesenchymal stem cells adhered to and readily proliferated on the three-dimensional fiber network, demonstrating that these polymers may find use in growing artificial blood vessels in vivo.