Vision-based classification of solder joint defects

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. The solder joint inspection problem is more challenging than many other visual inspections because of the variability in the appearance of solder joints. Although many research works and various techniques have been developed to classify defect in solder joints, these methods have complex systems of illumination for image acquisition and complicated classification algorithms. An important stage of the analysis is to select the right method for the classification. Better inspection technologies are needed to fill the gap between available inspection capabilities and industry systems. This dissertation aims to provide a solution that can overcome some of the limitations of current inspection techniques. This research proposes two inspection steps for automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localization and segmentation. The illumination normalisation approach can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image. The “back-end” inspection involves the classification of solder joints by using Log Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. Log Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. Further testing demonstrates the advantage of Log Gabor filter over both Discrete Wavelet Transform and Discrete Cosine Transform. Classifier score fusion is analysed for improving recognition rate. Experimental results demonstrate that the proposed system improves performance and robustness in terms of classification rates. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. In fact, the choice of suitable features allows one to overcome the problem given by the use of non complex illumination systems. The new system proposed in this research can be incorporated in the development of an automated non-contact, non-destructive and low cost solder joint quality inspection system.

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

Children, childhood and the contemporary media

This chapter investigates and critiques the idea of the sexualization of children in the contemporary media with a focus on recent events in Australia. It begins by commenting about aspects of Corporate Paedophilia: Sexualisation of children in Australia (Rush & La Nauze, 2006a) and then investigates relevant literature about consuming bodies to provide a frame for discussing consumer culture, children and childhood. Following this, the sexualization of children in the contemporary media is explored from the perspective of moral panics and the discourses of neoliberal tolerance and intolerance. The chapter concludes that although the idea of children being sexualized in contemporary media is contested, there can be no simple explanations and that a multiplicity of factors need to be taken into account that exist outside of media discourses.

A two-Dimensional finite volume method for variable density flow and solute transport through saturated-unsaturated media

Extensive groundwater withdrawal has resulted in a severe seawater intrusion problem in the Gooburrum aquifers at Bundaberg, Queensland, Australia. Better management strategies can be implemented by understanding the seawater intrusion processes in those aquifers. To study the seawater intrusion process in the region, a two-dimensional density-dependent, saturated and unsaturated flow and transport computational model is used. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt. A two-dimensional control volume finite element model is developed for simulating the seawater intrusion into the heterogeneous aquifer system at Gooburrum. The simulation results provide a realistic mechanism by which to study the convoluted transport phenomena evolving in this complex heterogeneous coastal aquifer.

What learners "know" through digital media production : learning by design

The power to influence others in ever-expanding social networks in the new knowledge economy is tied to capabilities with digital media production that require increased technological knowledge. This article draws on research in elementary classrooms to examine the repertoires of cross-disciplinary knowledge that literacy learners need to produce innovative digital media via the “social web”. The article builds on Learning by Design and the Knowledge Processes to describe “how” learning occurs, while presenting a model to theorise “what” students know – the Knowledge Assets – when learners produce digital and multimodal texts.

Transnational business masculinities : media representations of gender and management in global mining corporations

This paper examines Australian media representations of the male managers of two global mining corporations, Rio Tinto and BHP Billiton. These organizations are transnational (or multinational) corporations with assets and/or operations across national boundaries (Dunning and Lundan, 2008), and indeed their respective Chief Executive Officers, Tom Albanese and Marius Kloppers are two of the most economically (and arguably politically) powerful in the world overseeing 37 000 and 39 000 employees internationally. With a 2008 profit of US$15.962 billion and assets of US$ 75.889 Billion BHP Billiton is the world's largest mining company. In terms of its profits and assets Rio Tinto ranks fourth in the world, but with operations in six countries (mainly Canada and Australia) and a 2008 profit of US$10.3 billion it is also emblematic of the transnational in that its ‘budget is larger than that of all but a few nations’ (Giddens, 2003, p. 62).

Visualizing digital media interactions : providing feedback on jam2jam AV performances

Instrumental music performance is a well-established case of real-time interaction with technology and, when extended to ensembles, of interaction with others. However, these interactions are fleeting and the opportunities to reflect on action is limited, even though audio and video recording has recently provided important opportunities in this regard. In this paper we report on research to further extend these reflective opportunities through the capture and visualization of gestural data collected during collaborative virtual performances; specifically using the digital media instrument Jam2jam AV and the specifically-developed visualization software Jam2jam AV Visualize. We discusses how such visualization may assist performance development and understanding. The discussion engages with issues of representation, authenticity of virtual experiences, intersubjectivity and wordless collaboration, and creativity support. Two usage scenarios are described showing that collaborative intent is evident in the data visualizations more clearly than in audio-visual recordings alone, indicating that the visualization of performance gestures can be an efficient way of identifying deliberate and co-operative performance behaviours.

Collaborative digital media performance with generative music systems

Music is inherently active and interactive. Like technologies before them, digital systems provide a range of enhanced music performance opportunities. In this paper we outline the educational advantages of ensemble performance in which generative media systems are integrated. As a concrete example, we focus on our work with the jam2jam system which uses generative music processes to enhance collaborative music making. We suggest that our research points toward a new class of activities that maintain the well established benefits of ensemble performance while adding cultural and pedagogical value by leveraging the capabilities and cachet of digital media practices.

From social butterfly to engaged citizen: Urban informatics, social media, ubiquitous computing, and mobile technology to support citizen engagement

Web applications such as blogs, wikis, video and photo sharing sites, and social networking systems have been termed ‘Web 2.0’ to highlight an arguably more open, collaborative, personalisable, and therefore more participatory internet experience than what had previously been possible. Giving rise to a culture of participation, an increasing number of these social applications are now available on mobile phones where they take advantage of device-specific features such as sensors, location and context awareness. This international volume of book chapters will make a contribution towards exploring and better understanding the opportunities and challenges provided by tools, interfaces, methods and practices of social and mobile technology that enable participation and engagement. It brings together an international group of academics and practitioners from a diverse range of disciplines such as computing and engineering, social sciences, digital media and human-computer interaction to critically examine a range of applications of social and mobile technology, such as social networking, mobile interaction, wikis, twitter, blogging, virtual worlds, shared displays and urban sceens, and their impact to foster community activism, civic engagement and cultural citizenship.

Proactive communication management beats hostile media exposure : training for multi-cultural community leaders in living with mass media

Proactive communication management instead of mortification in the glare of hostile media attention became the theme of a four-day training program for multi-cultural community leaders, the object of this research. The program in Brisbane from December 2009 through to February this year was conducted under auspices of a Community Media Link grant program shared by Griffith University and the Queensland Ethnic Communities Council, together with Journalism academics from the Queensland University of Technology. Twenty-eight participants from 23 organisations took part, with a team of nine facilitators from the host organisations, and guest presenters from the news media. This paper reviews the process, taking into account: its objectives, to empower participants by showing how Australian media operate and introducing participants to journalists; pedagogical thrust, where overview talks, with role play seminars with guest presenters from the media, were combined with practice in interviews and writing for media; and outcomes, assessed on the basis of participants’ responses. The research methodology is qualitative, in that the study is based on discussions to review the planning and experience of sessions, and anonymous, informal feed-back questionnaires distributed to the participants. Background literature on multiculturalism and community media was referred to in the study. The findings indicate positive outcomes for participants from this approach to protection of persons unversed in living in the Australian “mediatised” environment. Most affirmed that the “production side” perspective of the exercise had informed and motivated them effectively, such that henceforth they would venture far more into media management, in their community leadership roles.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.