Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation

This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

That's a rap : the making of an Australian hip hop integrated musical film

To date, the majority of films that utilise or feature hip hop music and culture, have either been in the realms of documentary, or in ‘show musicals’ (where the film musical’s device of characters’ bursting into song, is justified by the narrative of a pursuit of a career in the entertainment industry). Thus, most films that feature hip hop expression have in some way been tied to the subject of hip hop. A research interest and enthusiasm was developed for utilising hip hop expression in film in a new way, which would extend the narrative possibilities of hip hop film to wider topics and themes. The creation of the thesis film Out of My Cloud, and the writing of this accompanying exegesis, investigates a research concern of the potential for the use of hip hop expression in an ‘integrated musical’ film (where characters’ break into song without conceit or explanation). Context and rationale for Out of My Cloud (an Australian hip hop ‘integrated musical’ film) is provided in this writing. It is argued that hip hop is particularly suitable for use in a modern narrative film, and particularly in an ‘integrated musical’ film, due to its: current vibrancy and popularity, rap (vocal element of hip hop) music’s focus on lyrical message and meaning, and rap’s use as an everyday, non-performative method of communication. It is also argued that Australian hip hop deserves greater representation in film and literature due to: its current popularity, and its nature as a unique and distinct form of hip hop. To date, representation of Australian hip hop in film and television has almost solely been restricted to the documentary form. Out of My Cloud borrows from elements of social realist cinema such as: contrasts with mainstream cinema, an exploration/recognition of the relationship between environment and development of character, use of non-actors, location-shooting, a political intent of the filmmaker, displaying sympathy for an underclass, representation of underrepresented character types and topics, and a loose narrative structure that does not offer solid resolution. A case is made that it may be appropriate to marry elements of social realist film with hip hop expression due to common characteristics, such as: representation of marginalised or underrepresented groups and issues in society, political objectives of the artist/s, and sympathy for an underclass. In developing and producing Out of My Cloud, a specific method of working with, and filming actor improvisation was developed. This method was informed by improvisation and associated camera techniques of filmmakers such as Charlie Chaplin, Mike Leigh, Khoa Do, Dogme 95 filmmakers, and Lars von Trier (post-Dogme 95). A review of techniques used by these filmmakers is provided in this writing, as well as the impact it has made on my approach. The method utilised in Out of My Cloud was most influenced by Khoa Do’s technique of guiding actors to improvise fairly loosely, but with a predetermined endpoint in mind. A variation of this technique was developed for use in Out of My Cloud, which involved filming with two cameras to allow edits from multiple angles. Specific processes for creating Out of My Cloud are described and explained in this writing. Particular attention is given to the approaches regarding the story elements and the music elements. Various significant aspects of the process are referred to including the filming and recording of live musical performances, the recording of ‘freestyle’ performances (lyrics composed and performed spontaneously) and the creation of a scored musical scene involving a vocal performance without regular timing or rhythm. The documentation of processes in this writing serve to make the successful elements of this film transferable and replicable to other practitioners in the field, whilst flagging missteps to allow fellow practitioners to avoid similar missteps in future projects. While Out of My Cloud is not without its shortcomings as a short film work (for example in the areas of story and camerawork) it provides a significant contribution to the field as a working example of how hip hop may be utilised in an ‘integrated musical’ film, as well as being a rare example of a narrative film that features Australian hip hop. This film and the accompanying exegesis provide insights that contribute to an understanding of techniques, theories and knowledge in the field of filmmaking practice.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

Feature-domain super-resolution for IRIS recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Superresolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values. This paper considers transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. This is the first paper to investigate the possibility of feature domain super-resolution for iris recognition, and experiments confirm the validity of the proposed approach.

Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Women and representation in local government : international case studies

Women and Representation in Local Government opens up an opportunity to critique and move beyond suppositions and labels in relation to women in local government. Presenting a wealth of new empirical material, this book brings together international experts to examine and compare the presence of women at this level and features case studies on the US, UK, France, Germany, Spain, Finland, Uganda, China, Australia and New Zealand. Divided into four main sections, each explores a key theme related to the subject of women and representation in local government and engages with contemporary gender theory and the broader literature on women and politics. The contributors explore local government as a gendered environment; critiquing strategies to address the limited number of elected female members in local government and examine the impact of significant recent changes on local government through a gender lens. Addressing key questions of how gender equality can be achieved in this sector, it will be of strong interest to students and academics working in the fields of gender studies, local government and international politics.