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<h2><a href="/research/biological/">Biological and biomedical data and image analysis</a></h2>
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Biological and biomedical data and image analysis The Group was involved in research to design of machine learning and soft computing based techniques for biological and biomedical data and image analysis: \
analysis of brain and skeletal NMR and TAC images. The careful delineation of the medical objects alone provides relevant clinical information and the extraction of quantitative features is fundamental, once the diagnosis was made, to determine the extent and progression of the disease.
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<h2><a href="/research/intelligent/">Intelligent Processing of Spatio-temporal signals</a></h2>
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Intelligent Processing of Spatio-temporal signals Clustering, that is discovery of groups of “similar” trajectories. As an example, the cluster of trajectories they can bring to light the presence of paths not adequately covered from the public transit service.
Frequent pattern, that is the discovery of frequent paths. These information could be useful for the city planning, as an example, evidencing frequently covered paths followed by vehicles, that could be the result of planning of the devoid traffic.
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<h2><a href="/research/soft/">Soft Computing In Image Analysis</a></h2>
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Soft Computing In Image Analysis The rise of several major seminal theories proposed in early 60’s including fuzzy logic, genetic algorithms, evolutionary computation, neural networks and their combination (the soft-computing paradigm in brief) allows to incorporate imprecision and incomplete information, and to model very complex systems, making them a useful tool in many scientific areas. These new methods may become more effective and powerful in real-world applications and can offer viable and effective solutions to some of the most difficult problems in image and pattern analysis.
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<h2><a href="/research/structured/">Structured Pattern Recognition</a></h2>
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Structured Pattern Recognition In machine learning, very powerful and efficient methods have been proposed when data are represented by flat and fixed-width real vectors, even when heavily corrupted by noise. Neural networks, support vector machines and statistical methods are well known and widely used techniques. All of them share many successful stories in real-life problems, a well established theoretical background, and many journals and conferences devoted to explore possible refinements and applications.
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<h2><a href="/research/video/">Video Surveillance</a></h2>
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Video surveillance The activity concerns the analysis, design and implementation of machine learning methods for the detection, tracking and real-time recognition of objects in motion sequences, also in mobile environments. Concerning real-time support, extensions of a video surveillance system have been proposed to make possible to guarantee speedup very close to the ideal, while improving the accuracy of the results detection.
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<h2><a href="/research/digital/">Digital Film Restoration</a></h2>
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Digital Film Restoration Activities concern several aspects of digital film restoration, including the analysis of issues related to the problem, ranging from the kind of different defects, to their causes, and to methods and algorithms for their removal. Particular attention is given to some specific types of defects that can affect digital image sequences and to methodologies adopted for their management, devising new machine learning based algorithms and methodologies for their removal.
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