0801 - Artificial Intelligence and Image Processing
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This collection contains Flinders' staff research in Artificial Intelligence and Image Processing, reportable as part of Excellence in Research for Australia (ERA), from 2001-
Items are added automatically from Flinders University Research Services Office.
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Item Sensor Fusion Weighting Measures in Audio-Visual Speech Recognition(Australian Computer Society, 2004) Powers, David Martin; Lewis, Trent WilsonItem Inward Looking Projections(Association for Computing Machinery, 2003) Vallance, Scott L; Calder, Paul RobertItem PETA: a pedagogical embodied teaching agent(ACM (Association for Computing Machinery), 2008) Leibbrandt, Richard Eduard; Powers, David Martin; Luerssen, Martin Holger; Lewis, Trent Wilson; Lawson, Mike JosephItem Establishing a lineage for medical knowledge discovery(Australian Computer Society, 2007) Roddick, John Francis; Shillabeer, AnnetteMedical science has a long history characterised by incidents of extraordinary insights that have resulted in a paradigm shift in the methodologies and approaches used and have moved the discipline forward. While knowledge discovery has much to offer medicine, it cannot be done in ignorance of either this history or the norms of modern medical investigation. This paper explores the lineage of medical knowledge acquisition and discusses the adverse perceptions that data mining techniques will have to surmount to gain acceptance.Item On the unproductiveness of language and linguistics(2006) Powers, David MartinItem An empirical investigation into grammatically constrained contexts in predicting distributional similarity(Australian Language Technology Association (ALTA), 2007) Powers, David Martin; Yang, DongqiangItem A unifying semantic distance model for determining the similarity of attribute values(Australian Computer Society, 2003) Roddick, John Francis; de Vries, Denise Bernadette; Hornsby, KathleenThe relative difference between two data values is of interest in a number of application domains including temporal and spatial applications, schema versioning, data warehousing (particularly data preparation), internet searching, validation and error correction, and data mining. Moreover, consistency across systems in determining such distances and the robustness of such calculations is essential in some domains and useful in many. Despite this, there is no generally adopted approach to determining such distances and no accommodation of distance within SQL or any commercially available DBMS. For non-numeric data values calculating the difference between values often requires application-specific support but even for numeric values the practical distance between two values may not simply be their numeric difference or Euclidean distance. In this paper, a model of semantic distance is developed in which a graph-based approach is used to quantify the distance between two data values. The approach facilitates a notion of distance, both as a simple traversal distance and as weighted arcs. Transition costs, as an additional expense of passing through a node, are also accommodated. Furthermore, multiple distance measures can be incorporated and a method of ‘localisation’ is discussed which allows relevant information to take precedence over less relevant information. Some results from our investigations, including our SQL based implementation, are presented.Item Experiences in building a tool for navigating association rule result sets(Australian Computer Society, 2004) Fule, Peter; Roddick, John FrancisPractical knowledge discovery is an iterative process. First, the experiences gained from one mining run are used to inform the parameter setting and the dataset and attribute selection for subsequent runs. Second, additional data, either incremental additions to existing datasets or the inclusion of additional attributes means that the mining process is reinvoked, perhaps numerous times. Reducing the number of iterations, improving the accuracy of parameter setting and making the results of the mining run more clearly understandable can thus significantly speed up the discovery process. In this paper we discuss our experiences in this area and present a system that helps the user to navigate through association rule result sets in a way that makes it easier to find useful results from a large result set. We present several techniques that experience has shown us to be useful. The prototype system – IRSetNav – is discussed, which has capabilities in redundant rule reduction, subjective interestingness evaluation, item and itemset pruning, related information searching, text-based itemset and rule visualisation, hierarchy based searching and tracking changes between data sets using a knowledge base. Techniques also discussed in the paper, but not yet accommodated into IRSetNav, include input schema selection, longitudinal ruleset analysis and graphical visualisation techniques.Item Haptic Rendering & Perception Studies for Laparoscopic Surgery Simulation(IEEE, 2006) Seligman, Cory; Alsaraira, Amer; Brown, Thomas Ian; Lim, Fabian; McColl, RyanItem Webcam Configurations for Ground Texture Visual Servo(IEEE Publishing, 2008) Powers, David Martin; Matsumoto, Takeshi; Asgari, NasserItem Facillitating database attribute domain evolution using mesodata(Springer, 2004) Roddick, John Francis; de Vries, Denise BernadetteItem CFA demosaicking with improved colour edge preservation(IEEE, 2005) Randhawa, Sherry; Li, Jimmy SiuItem Postponing schema definition: Low Instance-to-Entity Ratio (LItER) modelling(Springer, 2007) Roddick, John Francis; de Vries, Denise Bernadette; Ceglar, Aaron John; La-Ongsri, SomluckItem Application of search algorithms to natural language processing(Australian Language Technology Associations, 2003) Powers, David Martin; Matsumoto, Takeshi; Jarrad, GItem Mining relationships between interacting episodes(SIAM, 2004) Roddick, John Francis; Mooney, Carl HowardItem Constrained ant colony optimization for data clustering(Springer, 2004) Roddick, John Francis; Chu, S-C; Pan, Jeng-Shyang; Su, C-JItem From rule visualisation to guided knowledge discovery(University of Technology, Sydney, 2003) Roddick, John Francis; Mooney, Carl Howard; Calder, Paul Robert; Ceglar, Aaron JohnItem A visual and graphic/haptic rendering model for hysteroscopic procedures(Australasian College of Physical Scientists and Engineers in Medicine, 2005) Lim, Fabian; Brown, Thomas Ian; McColl, Ryan; Seligman, Cory; Alsaraira, AmerItem A multi-level framework for the analysis of sequential data(UTS, 2004) Roddick, John Francis; Mooney, Carl Howard; de Vries, Denise BernadetteItem Adaptive compression-based approach for Chinese Pinyin input(Association for Computational Liguistics, 2004) Powers, David Martin; Huang, Jin Hu