Superpixel-based appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Exploiting parallax in panoramic capture to construct light fields

We show that the parallax motion resulting from non-nodal rotation in panorama capture can be exploited for light field construction from commodity hardware. Automated panoramic image capture typically seeks to rotate a camera exactly about its nodal point, for which no parallax motion is observed. This can be difficult or impossible to achieve due to limitations of the mounting or optical systems, and consequently a wide range of captured panoramas suffer from parallax between images. We show that by capturing such imagery over a regular grid of camera poses, then appropriately transforming the captured imagery to a common parameterisation, a light field can be constructed. The resulting four-dimensional image encodes scene geometry as well as texture, allowing an increasingly rich range of light field processing techniques to be applied. Employing an Ocular Robotics REV25 camera pointing system, we demonstrate light field capture,refocusing and low-light image enhancement.

The Interview: Schnarking, Knob Jokes and the right to cause gross offence

Discussion of censorship and media freedom in the context of The Interview. A few weeks before the murderous attack by Islamic extremists on the satirical journal Charlie Hebdo, the Hollywood dream factory had its own encounter with would-be censors. The Interview (Evan Goldberg and Seth Rogen, 2014), as everyone with an interest in culture and current affairs cannot fail to be aware of by now, is a comedy in the “grossout” tradition exemplified by commercially successful movies such as Ted (Seth MacFarlane, 2012) and Bridesmaids (Paul Feig, 2011). Their humour is a combination of slapstick, physical comedy, and scatological jokes involving body fluids and the like— hence the “gross”. The best of them have been very funny, as well as bordering on the offensive (see Ted’s scene involving prostitutes, a foul-mouthed teddy bear and the entertainment value of someone taking a dump on the living room floor). They have often been controversial, as in the Farrelly brothers’ Me, Myself and Irene (2000), starring Jim Carrey as a schizophrenic police officer. At their most outrageous they have pushed the boundaries of political correctness to the limit.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Noise in my head: Voices from the ugly Australian underground

The Ugly Australian Underground documents the music, songwriting, aesthetics and struggles of fifty of Australia’s most innovative and significant bands and artists currently at the creative peak of their careers. The book provides a rare insight into the critically heralded cult music scene in Australia. The author, Jimi Kritzler, is both a journalist and a musician, and is personally connected to the musicians he interviews through his involvement in this music subculture. The interviews are extremely personal and reveal much more than any interview granted to street press or blogs. They deal with not only the music and songwriting processes of each band, but in some circumstances their struggles with drugs, involvement in crime and the death of band members.

Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.

An Intelligent Driver Warning System

This chapter is focussed on the research and development of an intelligent driver warning system (IDWS) as a means to improve road safety and driving comfort. Two independent IDWS case studies are presented. The first study examines the methodology and implementation for attentive visual tracking and trajectory estimation for dynamic scene segmentation problems. In the second case study, the concept of driver modelling is evaluated which can be used to provide useful feedback to drivers. In both case studies, the quality of IDWS is largely determined by the modelling capability for estimating multiple vehicle trajectories and modelling driving behaviour. A class of modelling techniques based on neural-fuzzy systems, which exhibits provable learning and modelling capability, is proposed. For complex modelling problems where the curse of dimensionality becomes an issue, a network construction algorithm based on Adaptive Spline Modelling of Observation Data (ASMOD) is also proposed.

The sweet aroma of success: Modification of products for new markets in the case of TORQ by Di Bella Coffee

In 2002, Phillip Di Bella’s childhood passion for coffee and keen entrepreneurial spirit led him to establish a small coffee roasting warehouse in in the inner suburbs of Brisbane (Di Bella, 2012). With a keen sense of direction and passion for his coffee products and providing unparalleled customer service, Di Bella Coffee quickly grew to become a key player in the coffee roasting scene. This passion for the ultimate coffee experience is evident in the firm’s logo ‘Di Bella Coffee Inspires Passion’. Phillip Di Bella stated that ‘the common denominator of this company is about inspiration and passion. We are not a coffee company, we are a people company. You know, are we inspiring you from the moment you walk in the door to the moment you leave. If you are not feeling inspired then we haven’t done our job properly as a company’. Fundamentally, providing the ultimate coffee experience, as detailed in the following case is one in which focuses on the coffee consumption experience, not the coffee itself. Over that last 10 years Di Bella Coffee has constantly strived for the ultimate coffee, while expanding business operations into the booming Asian coffee market, establishing headquarters in Shanghai in 2010. In 2011, Di Bella Coffee commenced their second international venture with the launch of operations in India (Di Bella Coffee, 2012); followed shortly by the creation of a new category of coffee, set to revolutionise to coffee industry. The fusion of two traditional forms of coffee; espresso coffee and instant coffee, to create a third category- espresso instant, led to the development of TORQ by Di Bella.

Planning, policy and you

The policy environment for regional natural resource management (NRM) has shifted considerably since it was first introduced in the early 2000s. This workshop will explore the impact of current policy on NRM planning and action through presentations and workshop discussion. To set the scene for the workshop discussion presentations will consider: 1) the impact of evolving national and state NRM policy in Australia; 2) how Australian NRM compares to other countries; 3) governance risks to NRM delivery; and 4) regional responses to NRM delivery. During the workshop element, participants from across regions will share their experiences and explore implications of current policy on the business of regional NRM.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

When Japanese law goes pop

Japanese law is going ‘pop’. Since the turn of the century, Japanese popular culture, especially prime-time television, has dedicated more time to legal themes, characters and settings. Lawyers, overwhelmingly women, are the heroes in both dramatic and comedic television series (Nakamura, 2007). Courtroom battles are the scene for plot developments (Ishikawa, 2004). Practising lawyers are the new celebrities, joining actors and singers on the light entertainment talk show circuit. To be sure, law is not a new thematic preoccupation on Japanese network television. Nor is it one that has become so dominant that it overshadows more traditional genres such as workplace romantic comedies, coming-of-age dramas or family soap operas (eg, Dissanayake, 2012, p._194). But, its growing presence on the silver screen in twenty-first-century Japan is a trend that merits analysis. The purpose of this chapter is to explore that socio-legal significance. This presents theoretical and empirical challenges. Theoretically, is there explanatory potential in the link between law and popular culture in Japan? Empirically, does the greater embrace of law-related characters, plots and scenes in prime-time television series since 2001 provide compelling evidence of changing popular attitudes to law and legal process among Japanese viewers? The inspiration for both the title and theme of this chapter comes from Sherwin’s When Law Goes Pop (2000). But it departs from Sherwin in how it defines and analyses the issues. For Sherwin, ‘pop’ means ‘implosion’.

Acute infectious diarrhea lessons learned from the past?

The Journal of Pediatric Gastroenterology and Nutrition (JPGN) has been at the forefront of many of the seminal advances into research on infectious diarrhea. In 1982, the first article of the JPGN was entitled “Oral Therapy for Dehydration in Diarrheal Diseases as a Global Problem” and has set the scene for several thousand subsequent articles. In his initial editorial, Finberg (1) posed several questions, which still have relevance 30 years later: 1. When is oral rehydration not appropriate, if ever? 2. What should be the composition of the oral solution and should there be more than one? 3. Should recommended practice be different in lesser-developed countries from those in developed countries? 4. Should the salts and glucose be prepackaged or should home supplies be used by instructed mothers? 5. When should standard feedings be resumed?

When film, fashion & interiors collide: Designing the After Darkly Graduate Fashion Show

In 2013 QUT Interior Design and Fashion Disciplines partnered to design the Catwalk for the QUT After Darkly Graduate Fashion Show. The ephemeral work (catwalk canopy and cinematic affects) was developed through collaboration between the authors based upon an undergraduate interior design unit ‘Filmic Interiors’ in which students were tasked with designing a fashion show. Filmic Interiors exploited the potential of film to influence, understand, and develop novel interior spaces through consideration of mise-en-scene, cinematic effects and atmospheric design strategies engaged by key film directors Jean Pierre Jeunet and Darren Aronofsky. The design outcome represents a hybridisation of student design proposals, contemplating both film and emerging collections from graduate fashion students. The work explored a number of iterations each testing material qualities and immaterial cinematic affects, as a means to develop new space. The process was led by experimentation undertaken by the designers through previous studio explorations surrounding the theme of ‘Strange Space’ and design practice ‘Making Strange’(Lindquist & Pytel, 2012). In doing so, the work paralleled the material formations of ‘obsessive collections’ and ‘making do’ evident in Jeunet’s scenography, rendering uncanny hybrid space (Ezra, 2008). Evocation of the immaterial found in much of director Aronofsky’s work, also became critical in the atmospheric experience intended for the show. This paper explores the process of collaboration and material experimentation in design, approached through a filmic lens. It provides insight into what happens when one enters into what can be termed an ‘ecology of production’, whereby the experimental making becomes the collaborative agent between designers, disciplines, and between stage and spectators. Finally it underlines the importance of ‘finding the work’ through material making and testing rather than through more controlled formalistic responses.

Where the action isn't, that's where it is

Contribution to ARI Remix. ARI remix is a three-year digital humanities, artist interviews and oral history project collecting and presenting memories of Australian Artist-run culture in Queensland, New South Wales and the Australian Capital Territory between 1980 and 2000. Its focus is fleshing out and illuminating the ephemeral and neglected histories of the many lively and socially engaged artistic scenes along the east coast of Australia during the last two decades of the 20th century.