ARDECO: Automatic Region DEtection and Conversion

We present Ardeco, a new algorithm for image abstraction and conversion from bitmap images into vector graphics. Given a bitmap image, our algorithm automatically computes the set of vector primitives and gradients that best approximates the image. In addition, more details can be generated in user-selected important regions, defined from eye-tracking data or from an importance map painted by the user. Our algorithm is based on a new two-level variational parametric segmentation algorithm, minimizing Mumford and Shah's energy and operating on an intermediate triangulation, well adapted to the features of the image

Performance optimisation of biological pathway data storage, retrieval, analysis and its interactive visualisation

The aim of this research was to optimise the performance of the storage, retrieval, analysis and interactive visualisation of biomolecular pathways data. This was achieved by the adoption of new technologies and a variety of highly optimised data structures, algorithms and strategies across the different layers of the software. The first challenge to overcome was the creation of a long-lasting, large-scale web application to enable pathways navigation; the Pathway Browser. This tool had to aggregate different modules to allow users to browse pathway content and use their own data to perform pathway analysis. Another challenge was the development of a high-performance pathway analysis tool to enable the analysis of genome-wide datasets within seconds. Once developed, it was also integrated into the Pathway Browser allowing interactive exploration and analysis of high throughput data. The Pathways Overview layout and widget were created to enable the representation of the complex parent-child relationships present in the pathways hierarchical organisation. This module provides a means to overlay analysis results in such a way that the user can easily distinguish the most significant areas of biology represented in their data. Although an existing force-directed layout algorithm was initially utilised for the graphical representation, it did not achieve the expected results and a custom radial layout algorithm was developed instead. A new version of the pathway Diagram Viewer was engineered to achieve loading and rendering of 97% of the target diagrams in less than 1 second. Combining the multi-layer HTML5 Canvas strategy with a space partitioning data structure minimised CPU workload, enabling the introduction of new features that further enhance user experience. On the server side, the work focused on the adoption of a graph database (Neo4j) and the creation of the new Content Service (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, enabled efficient access to the complex pathway data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%

SVG Rendering for Internet Imaging

allows representing complex graphical scenes by a collection of graphic vectorial-based primitives, offering several advantages with respect to classical raster images such as: scalability, resolution independence, etc. In this paper we present a full comparison between some advanced raster to SVG algorithms: SWaterG, SVGenie, SVGWave and some commercial tools. SWaterG works by a watershed decomposition coupled with some ad-hoc heuristics, SVGenie and SVGWave use a polygonalization based respectively on Data Dependent and Wavelet triangulation. The results obtained by SWaterG, SVGenie and SVGWave are satisfactory both in terms of perceptual measured quality and compression ratio