The Robust Reading Competition Annotation and Evaluation Platform

The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become a de-facto evaluation standard for robust reading systems and algorithms. Concurrent with its second incarnation in 2011, a continuous effort started to develop an on-line framework to facilitate the hosting and management of competitions. This paper outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the competitions. The RRC Annotation and Evaluation Platform is a modular framework, fully accessible through on-line interfaces. It comprises a collection of tools and services for managing all processes involved with defining and evaluating a research task, from dataset definition to annotation management, evaluation specification and results analysis. Although the framework has been designed with robust reading research in mind, many of the provided tools are generic by design. All aspects of the RRC Annotation and Evaluation Framework are available for research use.Comment: 6 pages, accepted to DAS 201

GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs

We present a prototype of a software tool for exploration of multiple combinatorial optimisation problems in large real-world and synthetic complex networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial Explorer), provides a unified framework for scalable computation and presentation of high-quality suboptimal solutions and bounds for a number of widely studied combinatorial optimisation problems. Efficient representation and applicability to large-scale graphs and complex networks are particularly considered in its design. The problems currently supported include maximum clique, graph colouring, maximum independent set, minimum vertex clique covering, minimum dominating set, as well as the longest simple cycle problem. Suboptimal solutions and intervals for optimal objective values are estimated using scalable heuristics. The tool is designed with extensibility in mind, with the view of further problems and both new fast and high-performance heuristics to be added in the future. GraphCombEx has already been successfully used as a support tool in a number of recent research studies using combinatorial optimisation to analyse complex networks, indicating its promise as a research software tool

Extraction of main levels of a building from a large point cloud

Horizontal levels are references entities, the base of man-made environments. Their creation is the first step for various applications including the BIM (Building Information Modelling). BIM is an emerging methodology, widely used for new constructions, and increasingly applied to existing buildings (scan-to-BIM). The as-built BIM process is still mainly manual or semi-automatic and therefore is highly time-consuming. The automation of the as-built BIM is a challenging topic among the research community. This study is part of an ongoing research into the scan-to-BIM process regarding the extraction of the principal structure of a building. More specifically, here we present a strategy to automatically detect the building levels from a large point cloud obtained with a terrestrial laser scanner survey. The identification of the horizontal planes is the first indispensable step to produce an as-built BIM model. Our algorithm, developed in C++, is based on plane extraction by means of the RANSAC algorithm followed by the minimization of the quadrate sum of points-plane distance. Moreover, this paper will take an in-depth look at the influence of data resolution in the accuracy of plane extraction and at the necessary accuracy for the construction of a BIM model. A laser scanner survey of a three floors building composed by 36 scan stations has produced a point cloud of about 550 million points. The estimated plane parameters at different data resolution are analysed in terms of distance from the full points cloud resolution

Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network

Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.Comment: Preprint for Journal Machine Learnin

Animating the evolution of software

The use and development of open source software has increased significantly in the last decade. The high frequency of changes and releases across a distributed environment requires good project management tools in order to control the process adequately. However, even with these tools in place, the nature of the development and the fact that developers will often work on many other projects simultaneously, means that the developers are unlikely to have a clear picture of the current state of the project at any time. Furthermore, the poor documentation associated with many projects has a detrimental effect when encouraging new developers to contribute to the software. A typical version control repository contains a mine of information that is not always obvious and not easy to comprehend in its raw form. However, presenting this historical data in a suitable format by using software visualisation techniques allows the evolution of the software over a number of releases to be shown. This allows the changes that have been made to the software to be identified clearly, thus ensuring that the effect of those changes will also be emphasised. This then enables both managers and developers to gain a more detailed view of the current state of the project. The visualisation of evolving software introduces a number of new issues. This thesis investigates some of these issues in detail, and recommends a number of solutions in order to alleviate the problems that may otherwise arise. The solutions are then demonstrated in the definition of two new visualisations. These use historical data contained within version control repositories to show the evolution of the software at a number of levels of granularity. Additionally, animation is used as an integral part of both visualisations - not only to show the evolution by representing the progression of time, but also to highlight the changes that have occurred. Previously, the use of animation within software visualisation has been primarily restricted to small-scale, hand generated visualisations. However, this thesis shows the viability of using animation within software visualisation with automated visualisations on a large scale. In addition, evaluation of the visualisations has shown that they are suitable for showing the changes that have occurred in the software over a period of time, and subsequently how the software has evolved. These visualisations are therefore suitable for use by developers and managers involved with open source software. In addition, they also provide a basis for future research in evolutionary visualisations, software evolution and open source development

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio