Voronoi image segmentation and its applications to geoinformatics

As various geospatial images are available for analysis, there is a strong need for an intelligent geospatial image processing method. Segmenting and districting digital images is a core process and is of great importance in many geo-related applications. We propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments images in O(N) time when N is the number of pixels. The algorithm is capable of handling generators of complex types (point, line and area), Minkowski metrics and different weights. This paper also provides applications of the proposed method in various geoinformation datasets. Illustrated examples demonstrate the usefulness and robustness of our proposed method

Age invariant face recognition system using automated voronoi diagram segmentation

One of the challenges in automatic face recognition is to achieve sequential face invariant. This is a challenging task because the human face undergoes many changes as a person grows older. In this study we will be focusing on age invariant features of a human face. The goal of this study is to investigate the face age invariant features that can be used for face matching, secondly is to come out with a prototype of matching scheme that is robust to the changes of facial aging and finally to evaluate the proposed prototype with the other similar prototype. The proposed approach is based on automated image segmentation using Voronoi Diagram (VD) and Delaunay Triangulations (DT). Later from the detected face region, the eyes will be detected using template matching together with DT. The outcomes, which are list of five coordinates, will be used to calculate interest distance in human faces. Later ratios between those distances are formulated. Difference vector will be use in the proposed method in order to perform face recognition steps. Datasets used for this research is selected images from FG-NET Aging Database and BioID Face Database, which is widely being used for image based face aging analysis; consist of 15 sample images taken from 5 different person. The selection is based on the project scopes and difference ages. The result shows that 11 images are successfully recognized. It shows an increase to 73.34% compared to other recent methods

Polygon Feature Extraction from Satellite Imagery Based on Colour Image Segmentation and Medial Axis

Areal features are of great importance in applications like shore line mapping, boundary delineation and change detection. This research work is an attempt to automate the process of extracting feature boundaries from satellite imagery. This process is intended to eventually replace manual digitization by computer assisted boundary detection and conversion to a vector layer in a Geographic Information System. Another potential application is to be able to use the extracted linear features in image matching algorithms. In multi-spectral satellite imagery, various features can be distinguished based on their colour. There has been a good amount of work already done as far as boundary detection and skeletonization is concerned, but this research work is different from the previous ones in the way that it uses the Delaunay graph and the Voronoi tessellation to extract boundary and skeletons that are guaranteed to be topologically equivalent to the segmented objects. The features thus extracted as object border can be stored as vector maps in a Geographic Information System after labelling and editing. Here we present a complete methodology of the skeletonization process from satellite imagery using a colour image segmentation algorithm with examples of road networks and hydrographic networks.

Point clouds to direct indoor pedestrian pathfinding

Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfacesUniversidade de Vigo | Ref. 00VI 131H 641.02Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-

Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales

Density-based clustering: algorithms and evaluation techniques

Density-based clustering algorithms involve a relevant subset of all the methods developed for cluster analysis, which is one of the fundamental pillars of unsupervised learning [2]. While the origins of clustering can be traced to the early 20th century [3], it is not until the 1990s that the concerns that would lead to develop density-based clustering algorithms are raised [4]. In 1996, the most popular density-based clustering algorithm to date (DBSCAN) is published [5] and, with it, many applications for density-based clustering are found within increasingly different fields over the next decades. In this introductory chapter, we present an overview of the research that led to this dissertation, focused mainly on density-based clustering. The work presented in this document can be divided into two main blocks, which, briefly stated, are: (1) research on the development of novel density-based algorithms and (2) research on evaluation techniques and metrics for density -based clustering. The motivation that led to this approach is expressed in Section 1.1. First, the original motivation to pursue the study of densitybased clustering algorithms (landmark discovery) is introduced in Section 1.1.1. After that, in Section 1.1.2, we explain the demand for an evaluation benchmark applicable to density-based clustering algorithms. In Section 1.2, the main objectives of this thesis, which emerge from the demands and opportunities introduced in the motivation section, are presented and justified. Subsequently, we introduce the main scientific contributions of this thesis (Section 1.3). A notation guide is then included to serve as a reference for the reader (Section 1.4). Lastly, the description regarding the structure of this document is included in Section 1.5.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Emilio Parrado Hernández.- Secretario: Fernando Fernández Martínez.- Vocal: Raúl Santos Rodrígue

Application of Geographic Information Systems

The importance of Geographic Information Systems (GIS) can hardly be overemphasized in today’s academic and professional arena. More professionals and academics have been using GIS than ever – urban & regional planners, civil engineers, geographers, spatial economists, sociologists, environmental scientists, criminal justice professionals, political scientists, and alike. As such, it is extremely important to understand the theories and applications of GIS in our teaching, professional work, and research. “The Application of Geographic Information Systems” presents research findings that explain GIS’s applications in different subfields of social sciences. With several case studies conducted in different parts of the world, the book blends together the theories of GIS and their practical implementations in different conditions. It deals with GIS’s application in the broad spectrum of geospatial analysis and modeling, water resources analysis, land use analysis, infrastructure network analysis like transportation and water distribution network, and such. The book is expected to be a useful source of knowledge to the users of GIS who envision its applications in their teaching and research. This easy-to-understand book is surely not the end in itself but a little contribution to toward our understanding of the rich and wonderful subject of GIS

Overlapping point cloud merge and surface reconstruction with parallel processing for real time application

Compañías mineras están en búsqueda constante de nuevas tecnologías para aumentar su productividad. Una de las tecnologías que les permite realizar la reconstrucción de la superficie sin poner en riesgo la vida de sus trabajadores es el uso de sensores LiDAR junto con plataformas móviles que les permiten rotar el sensor para realizar un escaneo completo de la estructura. Sin embargo, el procesamiento de los datos se realiza a través de ordenadores situados fuera de la mina, debido a su alto coste computacional, lo que se traduce en un alto coste de tiempo. En esta tesis presento como objetivo principal el diseño de un algoritmo paralelo para la fusión de nubes de puntos capturadas por un LiDAR y la reconstrucción de la superficie en tiempo real, con el fin de reducir el tiempo de procesado, teniendo en cuenta información a priori del patrón de barrido de los puntos. En la literatura se pueden encontrar algoritmos para la reducción de la densidad de puntos, sin embargo, en esta tesis, propongo la idea de suprimir estos puntos basándome en el principio de que la etapa de registro entre cada escaneo puede ser obtenida por un sistema de medición correctamente establecido, por lo tanto, no es necesario utilizar ningún algoritmo ICP. Además, a diferencia de los algoritmos genéricos de reconstrucción de superficies, propongo un nuevo algoritmo que utiliza la información a priori del sistema de escaneo que permite obtener la reconstrucción triangular en un tiempo menor al tiempo de escaneo del LiDAR. Este algoritmo se implementará en un ordenador desktop con el uso de GPUs proporcionadas por NVIDIA para evaluar su rendimiento y, también, se implementará en una Jetson Nano con datos de una mina socavón real. Finalmente, proporcionaré algunas recomendaciones y consideraciones a tener en cuenta en las etapas de evaluación del algoritmo secuencial, codificación del algoritmo paralelo e implementación en GPUs.Mining companies are constantly searching for new technologies in order to increase their productivity. One of the technologies that allow them to perform surface reconstruction without risking the lives of their workers is the use of LiDAR sensors in conjunction with mobile platforms that allow them to rotate the sensor to perform a full scan of the structure. However, the data processing is done through computers located outside the mine, due to its high computational cost, resulting in a high cost of time. This thesis presents as principal objective the design of a parallel algorithm for the fusion of point clouds captured by a LiDAR and the surface reconstruction in real-time, in order to reduce the time processing, taking into account a priori information of the scanning pattern of the points. Algorithms for point density reduction can be found in the literature, however, in this thesis these points are suppressed based on the principle that the registration stage between each scan can be obtained by a measurement system properly stablished, therefore, it is not necessary to use any ICP algorithm. Also, unlike the generic surface reconstruction algorithms, a new algorithm that uses the a priori information of the scanning system is proposed and allows to obtain the triangular mesh in real-time in comparison to the LiDAR scanning time. This algorithm will be implemented in a desktop computer with the use of GPUs provided by NVIDIA to evaluate its performance and, also, will be implemented in a Jetson Nano with real data. Finally, some recommendations and considerations are provided to be taken into account in the stages of evaluation of the sequential algorithm, coding of the parallel algorithm and implementation on GPUs