Development of a Size-Based Multiple Erosion Technique to Estimate the Aggregate Gradation in an Asphalt Mixture

Image processing (IP) techniques have recently been applied in the field of asphalt materials to help identify aggregate particles and measure their geometrical information based on sectional images of the material. This study examined IP techniques to improve the accuracy of analyzing the size distribution of aggregates in an asphalt mixture, and proposed two new methods: seven-layer overlaying (SLO) and size-based multiple erosion (SBME) to solve the problem of identifying connected aggregate particles that often occurs in typical IP applications. The proposed methods were tested for their effectiveness with a typical IP method using a referenced sectional image of asphalt mixture. Both the proposed methods successfully improved the accuracy of detection (number and size distribution) of aggregate particles, but the SBME approach was superior over the SLO method.Image processing (IP) techniques have recently been applied in the field of asphalt materials to help identify aggregate particles and measure their geometrical information based on sectional images of the material. This study examined IP techniques to improve the accuracy of analyzing the size distribution of aggregates in an asphalt mixture, and proposed two new methods: seven-layer overlaying (SLO) and size-based multiple erosion (SBME) to solve the problem of identifying connected aggregate particles that often occurs in typical IP applications. The proposed methods were tested for their effectiveness with a typical IP method using a referenced sectional image of asphalt mixture. Both the proposed methods successfully improved the accuracy of detection (number and size distribution) of aggregate particles, but the SBME approach was superior over the SLO method

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Extracção automática de dados georreferenciados a partir dos planos cadastrais portugueses

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009Image recognition algorithms are used to extract information from digitized images automatically. Systems designed to convert paper documents into meaningful vectorial representations are numerous nowadays, and have been constantly improved over the two last decades. However, none of these systems seems to be able to provide satisfying results when it comes to convert complex documents such as technical drawings, usually semantic of the problem is not considered and post-processing costs remain high. This dissertation presents a set of techniques that greatly simplifies the automatic extraction of cadastral entities from the portuguese cadastral maps. The validity of the approach is illustrated designing a prototype system, joining all recognition algorithms and validating all information.Fundação para a Ciência e Tecnologia (FCT