Navigating, recognizing and describing urban spaces with vision and lasers

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

An exercise intervention for women undergoing chemotherapy for ovarian cancer : feasibility and preliminary outcomes

Exercise interventions during adjuvant cancer treatment have been shown to increase functional capacity, relieve fatigue and distress and in one recent study, assist chemotherapy completion. These studies have been limited to breast, prostate or mixed cancer groups and it is not yet known if a similar intervention is even feasible among women diagnosed with ovarian cancer. Women undergoing treatment for ovarian cancer commonly have extensive pelvic surgery followed by high intensity chemotherapy. It is hypothesized that women with ovarian cancer may benefit most from a customised exercise intervention during chemotherapy treatment. This could reduce the number and severity of chemotherapy-related side-effects and optimize treatment adherence. Hence, the aim of the research was to assess feasibility and acceptability of a walking intervention in women with ovarian cancer whilst undergoing chemotherapy, as well as pre-post intervention changes in a range of physical and psychological outcomes. Newly diagnosed women with ovarian cancer were recruited from the Royal Brisbane and Women’s Hospital (RBWH), to participate in a walking program throughout chemotherapy. The study used a one group pre- post-intervention test design. Baseline (conducted following surgery but prior to the first or second chemotherapy cycles) and follow-up (conducted three weeks after the last chemotherapy dose was received) assessments were performed. To accommodate changes in side-effects associated with treatment, specific weekly walking targets with respect to frequency, intensity and duration, were individualised for each participant. To assess feasibility, adherence and compliance with prescribed walking sessions, withdrawals and adverse events were recorded. Physical and psychological outcomes assessed included functional capacity, body composition, anxiety and depression, symptoms experienced during treatment and quality of life. Chemotherapy completion data was also documented and self-reported program helpfulness was assessed using a questionnaire post intervention. Forty-two women were invited to participate. Nine women were recruited, all of whom completed the program. There were no adverse events associated with participating in the intervention and all women reported that the walking program was helpful during their neo-adjuvant or adjuvant chemotherapy treatment. Adherence and compliance to the walking prescription was high. On average, women achieved at least two of their three individual weekly prescription targets 83% of the time (range 42% to 94%). Positive changes were found in functional capacity and quality of life, in addition to reductions in the number and intensity of treatment-associated symptoms over the course of the intervention period. Functional capacity increased for all nine women from baseline to follow-up assessment, with improvements ranging from 10% to 51%. Quality of life improvements were also noted, especially in the physical well-being scale (baseline: median 18; follow-up: median 23). Treatment symptoms reduced in presence and severity, specifically, in constipation, pain and fatigue, post intervention. These positive yet preliminary results suggest that a walking intervention for women receiving chemotherapy for ovarian cancer is safe, feasible and acceptable. Importantly, women perceived the program to be helpful and rewarding, despite being conducted during a time typically associated with elevated distress and treatment symptoms that are often severe enough to alter or cease chemotherapy prescription.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

The Drinking Expectancy Questionnaire for Men who have Sex with Men (DEQ-MSM): A measure of substance-related beliefs

Introduction and Aims. Alcohol expectancies are associated with drinking behaviour and post-drinking use thoughts, feelings and behaviours. The expectancies held by specific cultural or sub-cultural groups have rarely been investigated. This research maps expectancies specific to gay and other men who have sex with men (MSM) and their relationship with substance use. This study describes the specific development of a measure of such beliefs for alcohol, the Drinking Expectancy Questionnaire for Men who have Sex with Men (DEQ-MSM). Design and Methods. Items selected through a focus group and interviews were piloted on 220 self-identified gay or other MSM via an online questionnaire. Results. Factor analysis revealed three distinct substance reinforcement domains ('Cognitive impairment', 'Sexual activity' and 'Social and emotional facilitation'). These factors were associated with consumption patterns of alcohol, and in a crucial test of discriminant validity were not associated with the consumption of cannabis or stimulants. Similarities and differences with existing measures will also be discussed. Discussion and Conclusions. The DEQ-MSM represents a reliable and valid measure of outcome expectancies, related to alcohol use among MSM, and represents an important advance as no known existing alcohol expectancy measure, to date, has been developed and/or normed for use among this group. Future applications of the DEQ-MSM in health promotion, clinical settings and research may contribute to reducing harm associated with alcohol use among MSM, including the development of alcohol use among young gay men.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Fundus image registration algorithms for a pc-based image processing system

We have developed digital image registration program for a MC 68000 based fundus image processing system (FIPS). FIPS not only is capable of executing typical image processing algorithms in spatial as well as Fourier domain, the execution time for many operations has been made much quicker by using a hybrid of "C", Fortran and MC6000 assembly languages.

Implementation of a survey form marking system using image processing techniques on an Imputer

This paper describes the feasibility of the application of an Imputer in a multiple choice answer sheet marking system based on image processing techniques.

A multi-resolution image alignment technique based on direct methods for pose estimation of aerial vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi- Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles’ state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle’s state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle’s state for more than one minute, at real-time frame rates based, only on visual information.