Ways of telling : research methodology for fashion textiles design

Research methodology in the discipline of Art & Design has been a topic for much debate in the academic community. The result of such avid and ongoing discussion appears to be a disciplinary obsession with research methodologies and a culture of adopting and adapting existing methodologies from more established disciplines. This has eventuated as a means of coping with academic criticism and as an attempt to elevate Art & Design to a ‘real academic status’. Whilst this adoption has had some effect in tempering the opinion of Art & Design research from more ‘serious’ academics the practice may be concealing a deeper problem for this discipline. Namely, that knowledge transfer within creative practice, particularly in fashion textiles design practice, is largely tacit in nature and not best suited to dissemination through traditional means of academic writing and publication. ----- ----- There is an opportunity to shift the academic debate away from appropriate (or inappropriate) use of methodologies and theories to demonstrate the existence (or absence) of rigor in creative practice research. In particular, the changing paradigms for the definitions of research to support new models for research quality assessment (such as the RAE in the United Kingdom and ERA in Australia) require a re-examination of the traditions of academic writing and publication in relation to this form of research. It is now appropriate to test the limits of tacit knowledge. It has been almost half a century since Michael Polanyi wrote “we know more than we can tell” (Polanyi, 1967 p.4) at a time when the only means of ‘telling’ was through academic writing and publishing in hardcopy format. ----- ----- This paper examines the academic debate surrounding research methodologies for fashion textiles design through auto-ethnographic case study and object analysis. The author argues that, while this debate is interesting, the focus should be to ask: are there more effective ways for creative practitioner researchers to disseminate their research? The aim of this research is to examine the possibilities of developing different, more effective methods of ‘telling’ to support the transfer of tacit knowledge inherent in the discipline of Fashion Textiles Design.

Luxury fashion : the significance of luxury fashion goods for sustainable fashion design

This paper extends the work of “Luxury fashion : the role of innovation as a key contributing factor in the development of luxury fashion goods and sustainable fashion design” (Finn, 2011). The discussion here begins with the observation that post consumer textile waste remains a major obstacle in realising a model of sustainable fashion design and production however, amongst the millions of tonnes of textile and clothing sent to landfill each year there is little evidence of authentic luxury branded goods ending life as landfill. The sustainable fashion movement often support approaches such as fashion up-cycle, re-cycle and cradle to cradle solutions. This paper argues that the priority should be to break the cycle of consumerism as an immediate intervention in ongoing unsustainable (and in some cases unethical) practices involved in the production of fashion goods. The connections between maker and consumer are explored through object analysis and the findings raise questions of the separation between luxury fashion goods and fashion goods that bear luxury fashion branding. This paper suggests that unethical and subversive exploitation of these connections may be used to promote increased consumerism while at the same time purporting exclusivity and superior craftsmanship.

Designing fashion : an exploration of practitioner research within the university environment

This thesis positions practitioner research within the emerging discipline of fashion and disputes that practitioner knowledge of fashion is predominantly tacit. This research contributes to the understanding of practitioner knowledge and proposes an object based model of practitioner research as an alternative to existing practice-led methodologies. The thesis theorises fashion objects as a site of significant knowledge and argues their potential to record and communicate fashion knowledge and disseminate practice-led research.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Analysis of Object-Specific Authorization Protocol (OSAP) using coloured petri nets - Version 1.0

Analyzing security protocols is an ongoing research in the last years. Different types of tools are developed to make the analysis process more precise, fast and easy. These tools consider security protocols as black boxes that can not easily be composed. It is difficult or impossible to do a low-level analysis or combine different tools with each other using these tools. This research uses Coloured Petri Nets (CPN) to analyze OSAP trusted computing protocol. The OSAP protocol is modeled in different levels and it is analyzed using state space method. The produced model can be combined with other trusted computing protocols in future works.

Analysis of Object-Specific Authorization Protocol (OSAP) using coloured Petri nets

The use of Trusted Platform Module (TPM) is be- coming increasingly popular in many security sys- tems. To access objects protected by TPM (such as cryptographic keys), several cryptographic proto- cols, such as the Object Specific Authorization Pro- tocol (OSAP), can be used. Given the sensitivity and the importance of those objects protected by TPM, the security of this protocol is vital. Formal meth- ods allow a precise and complete analysis of crypto- graphic protocols such that their security properties can be asserted with high assurance. Unfortunately, formal verification of these protocols are limited, de- spite the abundance of formal tools that one can use. In this paper, we demonstrate the use of Coloured Petri Nets (CPN) - a type of formal technique, to formally model the OSAP. Using this model, we then verify the authentication property of this protocol us- ing the state space analysis technique. The results of analysis demonstrates that as reported by Chen and Ryan the authentication property of OSAP can be violated.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Design and evaluation of an image analysis platform for low-power, low-bandwidth camera networks

We describe the design and evaluation of a platform for networks of cameras in low-bandwidth, low-power sensor networks. In our work to date we have investigated two different DSP hardware/software platforms for undertaking the tasks of compression and object detection and tracking. We compare the relative merits of each of the hardware and software platforms in terms of both performance and energy consumption. Finally we discuss what we believe are the ongoing research questions for image processing in WSNs.

Dynamic Performance Measures for Object Tracking Systems

Performance evaluation of object tracking systems is typically performed after the data has been processed, by comparing tracking results to ground truth. Whilst this approach is fine when performing offline testing, it does not allow for real-time analysis of the systems performance, which may be of use for live systems to either automatically tune the system or report reliability. In this paper, we propose three metrics that can be used to dynamically asses the performance of an object tracking system. Outputs and results from various stages in the tracking system are used to obtain measures that indicate the performance of motion segmentation, object detection and object matching. The proposed dynamic metrics are shown to accurately indicate tracking errors when visually comparing metric results to tracking output, and are shown to display similar trends to the ETISEO metrics when comparing different tracking configurations.

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.

The road safety implications of illegal street racing and associated risky driving behaviours : an analysis of offences and offenders

The purpose of this study was to explore the road safety implications of illegal street racing and associated risky driving behaviours. This issue was considered in two ways: Phase 1 examined the descriptions of 848 illegal street racing and associated risky driving offences that occurred in Queensland, Australia, in order to estimate the risk associated with these behaviours; and Phase 2 examined the traffic and crash histories of the 802 male offenders involved in these offences, and compared them to those of an age-matched comparison group, in order to examine the risk associated with the driver. It was found in Phase 1 that only 3.7% of these offences resulted in a crash (none of which were fatal), and that these crashes tended to be single-vehicle crashes where the driver lost control of the vehicle and collided with a fixed object. Phase 2 found that the offender sample had significantly more traffic infringements, licence sanctions and crashes in the previous three years than the comparison group. It was concluded that while only a small proportion of racing and associated offences result in a crash, these offenders appear to be generally risky drivers that warrant special attention.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.