Geomatics support to the metric documentation of the archaeological heritage. Tests and validations on the use of low-cost, rapid, image-based sensors and systems.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Condition Assessment of Concrete Bridge Decks Using Ground and Airborne Infrared Thermography

Applications of nondestructive testing (NDT) technologies have shown promise in assessing the condition of existing concrete bridges. Infrared thermography (IRT) has gradually gained wider acceptance as a NDT and evaluation tool in the civil engineering field. The high capability of IRT in detecting subsurface delamination, commercial availability of infrared cameras, lower cost compared with other technologies, speed of data collection, and remote sensing are some of the expected benefits of applying this technique in bridge deck inspection practices. The research conducted in this thesis aims at developing a rational condition assessment system for concrete bridge decks based on IRT technology, and automating its analysis process in order to add this invaluable technique to the bridge inspector’s tool box. Ground penetrating radar (GPR) has also been vastly recognized as a NDT technique capable of evaluating the potential of active corrosion. Therefore, integrating IRT and GPR results in this research provides more precise assessments of bridge deck conditions. In addition, the research aims to establish a unique link between NDT technologies and inspector findings by developing a novel bridge deck condition rating index (BDCI). The proposed procedure captures the integrated results of IRT and GPR techniques, along with visual inspection judgements, thus overcoming the inherent scientific uncertainties of this process. Finally, the research aims to explore the potential application of unmanned aerial vehicle (UAV) infrared thermography for detecting hidden defects in concrete bridge decks. The NDT work in this thesis was conducted on full-scale deteriorated reinforced concrete bridge decks located in Montreal, Quebec and London, Ontario. The proposed models have been validated through various case studies. IRT, either from the ground or by utilizing a UAV with high-resolution thermal infrared imagery, was found to be an appropriate technology for inspecting and precisely detecting subsurface anomalies in concrete bridge decks. The proposed analysis produced thermal mosaic maps from the individual IR images. The k-means clustering classification technique was utilized to segment the mosaics and identify objective thresholds and, hence, to delineate different categories of delamination severity in the entire bridge decks. The proposed integration methodology of NDT technologies and visual inspection results provided more reliable BDCI. The information that was sought to identify the parameters affecting the integration process was gathered from bridge engineers with extensive experience and intuition. The analysis process utilized the fuzzy set theory to account for uncertainties and imprecision in the measurements of bridge deck defects detected by IRT and GPR testing along with bridge inspector observations. The developed system and models should stimulate wider acceptance of IRT as a rapid, systematic and cost-effective evaluation technique for detecting bridge deck delaminations. The proposed combination of IRT and GPR results should expand their correlative use in bridge deck inspection. Integrating the proposed BDCI procedure with existing bridge management systems can provide a detailed and timely picture of bridge health, thus helping transportation agencies in identifying critical deficiencies at various service life stages. Consequently, this can yield sizeable reductions in bridge inspection costs, effective allocation of limited maintenance and repair funds, and promote the safety, mobility, longevity, and reliability of our highway transportation assets

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Crop plant reconstruction and feature extraction based on 3-D vision

3-D imaging is increasingly affordable and offers new possibilities for a more efficient agricul-tural practice with the use of highly advances technological devices. Some reasons contrib-uting to this possibility include the continuous increase in computer processing power, the de-crease in cost and size of electronics, the increase in solid state illumination efficiency and the need for greater knowledge and care of the individual crops. The implementation of 3-D im-aging systems in agriculture is impeded by the economic justification of using expensive de-vices for producing relative low-cost seasonal products. However, this may no longer be true since low-cost 3-D sensors, such as the one used in this work, with advance technical capabili-ties are already available. The aim of this cumulative dissertation was to develop new methodologies to reconstruct the 3-D shape of agricultural environment in order to recognized and quantitatively describe struc-tures, in this case: maize plants, for agricultural applications such as plant breeding and preci-sion farming. To fulfil this aim a comprehensive review of the 3-D imaging systems in agricul-tural applications was done to select a sensor that was affordable and has not been fully inves-tigated in agricultural environments. A low-cost TOF sensor was selected to obtain 3-D data of maize plants and a new adaptive methodology was proposed for point cloud rigid registra-tion and stitching. The resulting maize 3-D point clouds were highly dense and generated in a cost-effective manner. The validation of the methodology showed that the plants were recon-structed with high accuracies and the qualitative analysis showed the visual variability of the plants depending on the 3-D perspective view. The generated point cloud was used to obtain information about the plant parameters (stem position and plant height) in order to quantita-tively describe the plant. The resulting plant stem positions were estimated with an average mean error and standard deviation of 27 mm and 14 mm, respectively. Additionally, meaning-ful information about the plant height profile was also provided, with an average overall mean error of 8.7 mm. Since the maize plants considered in this research were highly heterogeneous in height, some of them had folded leaves and were planted with standard deviations that emulate the real performance of a seeder; it can be said that the experimental maize setup was a difficult scenario. Therefore, a better performance, for both, plant stem position and height estimation could be expected for a maize field in better conditions. Finally, having a 3-D re-construction of the maize plants using a cost-effective sensor, mounted on a small electric-motor-driven robotic platform, means that the cost (either economic, energetic or time) of gen-erating every point in the point cloud is greatly reduced compared with previous researches.Die 3D-Bilderfassung ist zunehmend kostengünstiger geworden und bietet neue Möglichkeiten für eine effizientere landwirtschaftliche Praxis durch den Einsatz hochentwickelter technologischer Geräte. Einige Gründe, die diese ermöglichen, ist das kontinuierliche Wachstum der Computerrechenleistung, die Kostenreduktion und Miniaturisierung der Elektronik, die erhöhte Beleuchtungseffizienz und die Notwendigkeit einer besseren Kenntnis und Pflege der einzelnen Pflanzen. Die Implementierung von 3-D-Sensoren in der Landwirtschaft wird durch die wirtschaftliche Rechtfertigung der Verwendung teurer Geräte zur Herstellung von kostengünstigen Saisonprodukten verhindert. Dies ist jedoch nicht mehr länger der Fall, da kostengünstige 3-D-Sensoren, bereits verfügbar sind. Wie derjenige dier in dieser Arbeit verwendet wurde. Das Ziel dieser kumulativen Dissertation war, neue Methoden für die Visualisierung die 3-D-Form der landwirtschaftlichen Umgebung zu entwickeln, um Strukturen quantitativ zu beschreiben: in diesem Fall Maispflanzen für landwirtschaftliche Anwendungen wie Pflanzenzüchtung und Precision Farming zu erkennen. Damit dieses Ziel erreicht wird, wurde eine umfassende Überprüfung der 3D-Bildgebungssysteme in landwirtschaftlichen Anwendungen durchgeführt, um einen Sensor auszuwählen, der erschwinglich und in landwirtschaftlichen Umgebungen noch nicht ausgiebig getestet wurde. Ein kostengünstiger TOF-Sensor wurde ausgewählt, um 3-D-Daten von Maispflanzen zu erhalten und eine neue adaptive Methodik wurde für die Ausrichtung von Punktwolken vorgeschlagen. Die resultierenden Mais-3-D-Punktwolken hatten eine hohe Punktedichte und waren in einer kosteneffektiven Weise erzeugt worden. Die Validierung der Methodik zeigte, dass die Pflanzen mit hoher Genauigkeit rekonstruiert wurden und die qualitative Analyse die visuelle Variabilität der Pflanzen in Abhängigkeit der 3-D-Perspektive zeigte. Die erzeugte Punktwolke wurde verwendet, um Informationen über die Pflanzenparameter (Stammposition und Pflanzenhöhe) zu erhalten, die die Pflanze quantitativ beschreibt. Die resultierenden Pflanzenstammpositionen wurden mit einem durchschnittlichen mittleren Fehler und einer Standardabweichung von 27 mm bzw. 14 mm berechnet. Zusätzlich wurden aussagekräftige Informationen zum Pflanzenhöhenprofil mit einem durchschnittlichen Gesamtfehler von 8,7 mm bereitgestellt. Da die untersuchten Maispflanzen in der Höhe sehr heterogen waren, hatten einige von ihnen gefaltete Blätter und wurden mit Standardabweichungen gepflanzt, die die tatsächliche Genauigkeit einer Sämaschine nachahmen. Man kann sagen, dass der experimentelle Versuch ein schwieriges Szenario war. Daher könnte für ein Maisfeld unter besseren Bedingungen eine besseres Resultat sowohl für die Pflanzenstammposition als auch für die Höhenschätzung erwartet werden. Schließlich bedeutet eine 3D-Rekonstruktion der Maispflanzen mit einem kostengünstigen Sensor, der auf einer kleinen elektrischen, motorbetriebenen Roboterplattform montiert ist, dass die Kosten (entweder wirtschaftlich, energetisch oder zeitlich) für die Erzeugung jedes Punktes in den Punktwolken im Vergleich zu früheren Untersuchungen stark reduziert werden

Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases

Development of an image based system for routine visual inspection of UK highways bridges

Accurate inspection data is important for efficient bridge management. Visual inspections play a key role in providing this information, but the reliability of such data has limitations. A range of techniques addressing these limitations are used in other sectors, but not to assist routine visual bridge inspection. Work has been undertaken investigating the feasibility of performing routine visual bridge inspections based on systematically collected images alone. The requirements of such a system are considered and defined. The research demonstrates that more detail can be seen in images at 1-pixel-per-mm than can be seen from 3m, and that images at this resolution can be systematically collected, processed, displayed, and inspected to complete General Inspections with results comparable to traditional routine visual inspections. No existing systems were found to be suitable for routinely providing visual inspection data; consequently a prototype was developed demonstrating the feasibility of the image-based inspection approach. The development considered hardware, image collection methodology, processing, alignment, display and interpretation. Inspectors tested and used the system to perform image-based General Inspections on several bridges. It is concluded that an image­based approach can be used to perform routine visual bridge inspections, with no loss of detail compared to traditional inspections

Focus stacking in UAV-based inspection

In UAV-based inspection the most common problems are motion blur and focusing issues. These problems are often due to low-light environment, which can be compensated to some extent with shorter exposure times by using larger apertures and luminous lenses. Large apertures lead to limited depth of field and a solution called focus stacking can be used to extent the focal depth. The main goal of this thesis was to find out the feasibility of focus stacking in UAV inspection and a prototype system was designed and implemented. The acquisition system was implemented with an industrial type camera and an electrical liquid polymer lens. The post-processing software was implemented with OpenCV computer vision library because libraries offer the best possibilities to affect the low-level functionality. Three algorithms were chosen for the image registration and three for the image fusion. In addition, improvements to the speed and accuracy of the registration were examined. The implemented system was compared to equivalent open-source applications in each phase and it outperformed those applications in general performance. The most important goal was achieved and the system managed to improve the image data. A sequential acquisition system is not the best option on moving platform due to the perspective changes causing artifacts in image fusion. Also the optical resolution of the liquid lens was not enough for high resolution inspection imaging. However the idea of focus stacking works and the best solution for a mobile platform would be a multi-sensor system capturing the images simultaneously

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases