Evaluating automatic road detection across a large aerial imagery collection

The automated extraction of roads from aerial imagery can be of value for tasks including mapping, surveillance and change detection. Unfortunately, there are no public databases or standard evaluation protocols for evaluating these techniques. Many techniques are further hindered by a reliance on manual initialisation, making large scale application of the techniques impractical. In this paper, we present a public database and evaluation protocol for the evaluation of road extraction algorithms, and propose an improved automatic seed finding technique to initialise road extraction, based on a combination of geometric and colour features.

Investigating the performance of automatic new topic identification across multiple datasets 1

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.

Using Web Search Logs to Identify Query Classification Terms

Purpose – The work presented in this paper aims to provide an approach to classifying web logs by personal properties of users. Design/methodology/approach – The authors describe an iterative system that begins with a small set of manually labeled terms, which are used to label queries from the log. A set of background knowledge related to these labeled queries is acquired by combining web search results on these queries. This background set is used to obtain many terms that are related to the classification task. The system then ranks each of the related terms, choosing those that most fit the personal properties of the users. These terms are then used to begin the next iteration. Findings – The authors identify the difficulties of classifying web logs, by approaching this problem from a machine learning perspective. By applying the approach developed, the authors are able to show that many queries in a large query log can be classified. Research limitations/implications – Testing results in this type of classification work is difficult, as the true personal properties of web users are unknown. Evaluation of the classification results in terms of the comparison of classified queries to well known age-related sites is a direction that is currently being exploring. Practical implications – This research is background work that can be incorporated in search engines or other web-based applications, to help marketing companies and advertisers. Originality/value – This research enhances the current state of knowledge in short-text classification and query log learning. Classification schemes, Computer networks, Information retrieval, Man-machine systems, User interfaces

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

Event-related brain potentials dissociate the developmental time-course of automatic numerical magnitude analysis and cognitive control functions during the first three years of primary school

In this study we set out to dissociate the developmental time course of automatic symbolic number processing and cognitive control functions in grade 1-3 British primary school children. Event-related potential (ERP) and behavioral data were collected in a physical size discrimination numerical Stroop task. Task-irrelevant numerical information was processed automatically already in grade 1. Weakening interference and strengthening facilitation indicated the parallel development of general cognitive control and automatic number processing. Relationships among ERP and behavioral effects suggest that control functions play a larger role in younger children and that automaticity of number processing increases from grade 1 to 3.

Learning informative features for indoor traversability

This paper presents a method for automatic terrain classification, using a cheap monocular camera in conjunction with a robot’s stall sensor. A first step is to have the robot generate a training set of labelled images. Several techniques are then evaluated for preprocessing the images, reducing their dimensionality, and building a classifier. Finally, the classifier is implemented and used online by an indoor robot. Results are presented, demonstrating an increased level of autonomy.

Computational approaches to the visual validation of 3D virtual environments

Virtual environments can provide, through digital games and online social interfaces, extremely exciting forms of interactive entertainment. Because of their capability in displaying and manipulating information in natural and intuitive ways, such environments have found extensive applications in decision support, education and training in the health and science domains amongst others. Currently, the burden of validating both the interactive functionality and visual consistency of a virtual environment content is entirely carried out by developers and play-testers. While considerable research has been conducted in assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. The aim of this thesis is to determine whether the correctness of the images generated by a virtual environment can be quantitatively defined, and automatically measured, in order to facilitate the validation of the content. In an attempt to provide an environment-independent definition of visual consistency, a number of classification approaches were developed. First, a novel model-based object description was proposed in order to enable reasoning about the color and geometry change of virtual entities during a play-session. From such an analysis, two view-based connectionist approaches were developed to map from geometry and color spaces to a single, environment-independent, geometric transformation space; we used such a mapping to predict the correct visualization of the scene. Finally, an appearance-based aliasing detector was developed to show how incorrectness too, can be quantified for debugging purposes. Since computer games heavily rely on the use of highly complex and interactive virtual worlds, they provide an excellent test bed against which to develop, calibrate and validate our techniques. Experiments were conducted on a game engine and other virtual worlds prototypes to determine the applicability and effectiveness of our algorithms. The results show that quantifying visual correctness in virtual scenes is a feasible enterprise, and that effective automatic bug detection can be performed through the techniques we have developed. We expect these techniques to find application in large 3D games and virtual world studios that require a scalable solution to testing their virtual world software and digital content.

Operations research for mining : a classification and literature review

In this paper, we describe the main processes and operations in mining industries and present a comprehensive survey of operations research methodologies that have been applied over the last several decades. The literature review is classified into four main categories: mine design; mine production; mine transportation; and mine evaluation. Mining design models are further separated according to two main mining methods: open-pit and underground. Moreover, mine production models are subcategorised into two groups: ore mining and coal mining. Mine transportation models are further partitioned in accordance with fleet management, truck haulage and train scheduling. Mine evaluation models are further subdivided into four clusters in terms of mining method selection, quality control, financial risks and environmental protection. The main characteristics of four Australian commercial mining software are addressed and compared. This paper bridges the gaps in the literature and motivates researchers to develop more applicable, realistic and comprehensive operations research models and solution techniques that are directly linked with mining industries.

Development of a high-speed sensing and detection system for automatic removal of packages with leaky seals from a high-speed food processing and packaging line

Contamination of packaged foods due to micro-organisms entering through air leaks can cause serious public health issues and cost companies large amounts of money due to product recalls, consumer impact and subsequent loss of market share. The main source of contamination is leaks in packaging which allow air, moisture and microorganisms to enter the package. In the food processing and packaging industry worldwide, there is an increasing demand for cost effective state of the art inspection technologies that are capable of reliably detecting leaky seals and delivering products at six-sigma. The new technology will develop non-destructive testing technology using digital imaging and sensing combined with a differential vacuum technique to assess seal integrity of food packages on a high-speed production line. The cost of leaky packages in Australian food industries is estimated close to AUD $35 Million per year. Contamination of packaged foods due to micro-organisms entering through air leaks can cause serious public health issues and cost companies large sums of money due to product recalls, compensation claims and loss of market share. The main source of contamination is leaks in packaging which allow air, moisture and micro-organisms to enter the package. Flexible plastic packages are widely used, and are the least expensive form of retaining the quality of the product. These packets can be used to seal, and therefore maximise, the shelf life of both dry and moist products. The seals of food packages need to be airtight so that the food content is not contaminated due to contact with microorganisms that enter as a result of air leakage. Airtight seals also extend the shelf life of packaged foods, and manufacturers attempt to prevent food products with leaky seals being sold to consumers. There are many current NDT (non-destructive testing) methods of testing the seal of flexible packages best suited to random sampling, and for laboratory purposes. The three most commonly used methods are vacuum/pressure decay, bubble test, and helium leak detection. Although these methods can detect very fine leaks, they are limited by their high processing time and are not viable in a production line. Two nondestructive in-line packaging inspection machines are currently available and are discussed in the literature review. The detailed design and development of the High-Speed Sensing and Detection System (HSDS) is the fundamental requirement of this project and the future prototype and production unit. Successful laboratory testing was completed and a methodical design procedure was needed for a successful concept. The Mechanical tests confirmed the vacuum hypothesis and seal integrity with good consistent results. Electrically, the testing also provided solid results to enable the researcher to move the project forward with a certain amount of confidence. The laboratory design testing allowed the researcher to confirm theoretical assumptions before moving into the detailed design phase. Discussion on the development of the alternative concepts in both mechanical and electrical disciplines enables the researcher to make an informed decision. Each major mechanical and electrical component is detailed through the research and design process. The design procedure methodically works through the various major functions both from a mechanical and electrical perspective. It opens up alternative ideas for the major components that although are sometimes not practical in this application, show that the researcher has exhausted all engineering and functionality thoughts. Further concepts were then designed and developed for the entire HSDS unit based on previous practice and theory. In the future, it would be envisaged that both the Prototype and Production version of the HSDS would utilise standard industry available components, manufactured and distributed locally. Future research and testing of the prototype unit could result in a successful trial unit being incorporated in a working food processing production environment. Recommendations and future works are discussed, along with options in other food processing and packaging disciplines, and other areas in the non-food processing industry.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Solder joint defects classification using the Log-Gabor Filter, the Discrete Wavelet Transform and the Discrete Cosine Transform

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure project quality and reliability. This paper proposes the use of the Log-Gabor filter bank, Discrete Wavelet Transform and Discrete Cosine Transform for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry.

ACE research vignette 003 : different strokes for different folks - classification of entrepreneurial musicians and their use of online social networks

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international entrepreneurship researchers. In this vignette Dr Maria Kaya and Associate Professor Paul Steffens consider both the classification of musicians and their use of online social networks.

A classification framework for design-build variants from an operational perspective

Design-build (DB) is a generic form of construction procurement, and, rather than simply representing a single system, it has evolved in practice into a variety of forms, each of which is similar to, and yet different from each other. Although the importance of selecting an appropriate DB variant has been widely accepted, difficulties occur in practice due to the multiplicity of terms and concepts used. What is needed is some kind of taxonomy or framework within which the individual variants can be placed and their relative attributes identified and understood. Through a comprehensive literature review and content analysis, this paper establishes a systematic classification framework for DB variants based on their operational attributes. In addition to providing much needed support for decision-making, this classification framework provides client/owners with perspectives to understand and examine different categories of DB variants from an operational perspective.