313 research outputs found

    Structure and Phase Transformations in Thin Films

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    Autonomisten metsäkoneiden koneaistijärjestelmät

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    A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä. Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen

    Super-Resolution Textured Digital Surface Map (DSM) Formation by Selecting the Texture From Multiple Perspective Texel Images Taken by a Low-Cost Small Unmanned Aerial Vehicle (UAV)

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    Textured Digital Surface Model (TDSM) is a three-dimensional terrain map with texture overlaid on it. Utah State University has developed a texel camera which can capture a 3D image called a texel image. A TDSM can be constructed by combining these multiple texel images, which is much cheaper than the traditional method. The overall goal is to create a TDSM for a larger area that is cheaper and equally accurate as the TDSM created using a high-cost system. The images obtained from such an inexpensive camera have a lot of errors. To create scientifically accurate TDSM, the error presented in the image must be corrected. An automatic process to create TDSM is presented that can handle a large number of input texel images. The advantage of using such a large set of input images is that they can cover a large area on the ground, making the algorithm suitable for large-scale applications. This is done by processing images and correcting them in a windowing manner. Furthermore, the appearance of the final 3D terrain map is improved by selecting the texture from many candidate images. This ensures that the best texture is selected. The selection criteria are discussed. Lastly, a method to increase the resolution of the final image is discussed. The methods described in this dissertation improve the current technique of creating TDSM, and the results are shown and analyzed

    Advances in Lubricated Bearings

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    This reprint features 12 research articles that contribute to the research on lubricated bearings. The articles focus on the latest steps in understanding bearing operating behavior, its interaction with lubricants, and its role as a component in the drive train. In addition to the description of novel modeling approaches, a variety of experimental data are presented to provide interesting identification results as well as validation data for the research and engineering community

    SATR: Zero-Shot Semantic Segmentation of 3D Shapes

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    We explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models. Surprisingly, we find that modern zero-shot 2D object detectors are better suited for this task than contemporary text/image similarity predictors or even zero-shot 2D segmentation networks. Our key finding is that it is possible to extract accurate 3D segmentation maps from multi-view bounding box predictions by using the topological properties of the underlying surface. For this, we develop the Segmentation Assignment with Topological Reweighting (SATR) algorithm and evaluate it on two challenging benchmarks: FAUST and ShapeNetPart. On these datasets, SATR achieves state-of-the-art performance and outperforms prior work by at least 22\% on average in terms of mIoU. Our source code and data will be publicly released. Project webpage: https://samir55.github.io/SATR/Comment: Project webpage: https://samir55.github.io/SATR

    An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

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    The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures

    Assisting digital volume correlation with mechanical image-based modeling: application to the measurement of kinematic fields at the architecture scale in cellular materials

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    La mesure de champs de déplacement et de déformation aux petites échelles dans des microstructures complexes représente encore un défi majeur dans le monde de la mécanique expérimentale. Ceci est en partie dû aux acquisitions d'images et à la pauvreté de la texture à ces échelles. C'est notamment le cas pour les matériaux cellulaires lorsqu'ils sont imagés avec des micro-tomographes conventionnels et qu'ils peuvent être sujets à des mécanismes de déformation complexes. Comme la validation de modèles numériques et l'identification des propriétés mécaniques de matériaux se base sur des mesures précises de déplacements et de déformations, la conception et l'implémentation d'algorithmes robustes et fiables de corrélation d'images semble nécessaire. Lorsque l'on s'intéresse à l'utilisation de la corrélation d'images volumiques (DVC) pour les matériaux cellulaires, on est confronté à un paradoxe: l'absence de texture à l'échelle du constituant conduit à considérer l'architecture comme marqueur pour la corrélation. Ceci conduit à l'échec des techniques ordinaires de DVC à mesurer des cinématiques aux échelles subcellulaires en lien avec des comportements mécaniques locaux complexes tels que la flexion ou le flambement de travées. L'objectif de cette thèse est la conception d'une technique de DVC pour la mesure de champs de déplacement dans des matériaux cellulaires à l'échelle de leurs architectures. Cette technique assiste la corrélation d'images par une régularisation élastique faible en utilisant un modèle mécanique généré automatiquement et basé sur les images. La méthode suggérée introduit une séparation d'échelles au dessus desquelles la DVC est dominante et en dessous desquelles elle est assistée par le modèle mécanique basé sur l'image. Une première étude numérique consistant à comparer différentes techniques de construction de modèles mécaniques basés sur les images est conduite. L'accent est mis sur deux méthodes de calcul particulières: la méthode des éléments finis (FEM) et la méthode des cellules finies (FCM) qui consiste à immerger la géométrie complexe dans une grille régulière de haut ordre sans utiliser de mailleurs. Si la FCM évite une première phase délicate de discrétisation, plusieurs paramètres restent néanmoins délicats à fixer. Dans ce travail, ces paramètres sont ajustés afin d'obtenir (a) la meilleure précision (bornée par les erreurs de pixellisation) tout en (b) assurant une complexité minimale. Pour l'aspect mesure par corrélation d'images régularisée, plusieurs expérimentations virtuelles à partir de différentes simulations numériques (en élasticité, en plasticité et en non-linéarité géométrique) sont d'abord réalisées afin d'analyser l'influence des paramètres de régularisation introduits. Les erreurs de mesures peuvent dans ce cas être quantifiées à l'aide des solutions de référence éléments finis. La capacité de la méthode à mesurer des cinématiques complexes en absence de texture est démontrée pour des régimes non-linéaires tels que le flambement. Finalement, le travail proposé est généralisé à la corrélation volumique des différents états de déformation du matériau et à la construction automatique de la micro-architecture cellulaire en utilisant soit une grille B-spline d'ordre arbitraire (FCM) soit un maillage éléments finis (FEM). Une mise en évidence expérimentale de l'efficacité et de la justesse de l'approche proposée est effectuée à travers de la mesure de cinématiques complexes dans une mousse polyuréthane sollicitée en compression lors d'un essai in situ.Measuring displacement and strain fields at low observable scales in complex microstructures still remains a challenge in experimental mechanics often because of the combination of low definition images with poor texture at this scale. The problem is particularly acute in the case of cellular materials, when imaged by conventional micro-tomographs, for which complex highly non-linear local phenomena can occur. As the validation of numerical models and the identification of mechanical properties of materials must rely on accurate measurements of displacement and strain fields, the design and implementation of robust and faithful image correlation algorithms must be conducted. With cellular materials, the use of digital volume correlation (DVC) faces a paradox: in the absence of markings of exploitable texture on/or in the struts or cell walls, the available speckle will be formed by the material architecture itself. This leads to the inability of classical DVC codes to measure kinematics at the cellular and a fortiori sub-cellular scales, precisely because the interpolation basis of the displacement field cannot account for the complexity of the underlying kinematics, especially when bending or buckling of beams or walls occurs. The objective of the thesis is to develop a DVC technique for the measurement of displacement fields in cellular materials at the scale of their architecture. The proposed solution consists in assisting DVC by a weak elastic regularization using an automatic image-based mechanical model. The proposed method introduces a separation of scales above which DVC is dominant and below which it is assisted by image-based modeling. First, a numerical investigation and comparison of different techniques for building automatically a geometric and mechanical model from tomographic images is conducted. Two particular methods are considered: the finite element method (FEM) and the finite-cell method (FCM). The FCM is a fictitious domain method that consists in immersing the complex geometry in a high order structured grid and does not require meshing. In this context, various discretization parameters appear delicate to choose. In this work, these parameters are adjusted to obtain (a) the best possible accuracy (bounded by pixelation errors) while (b) ensuring minimal complexity. Concerning the ability of the mechanical image-based models to regularize DIC, several virtual experimentations are performed in two-dimensions in order to finely analyze the influence of the introduced regularization lengths for different input mechanical behaviors (elastic, elasto-plastic and geometrically non-linear) and in comparison with ground truth. We show that the method can estimate complex local displacement and strain fields with speckle-free low definition images, even in non-linear regimes such as local buckling. Finally a three-dimensional generalization is performed through the development of a DVC framework. It takes as an input the reconstructed volumes at the different deformation states of the material and constructs automatically the cellular micro-architeture geometry. It considers either an immersed structured B-spline grid of arbitrary order or a finite-element mesh. An experimental evidence is performed by measuring the complex kinematics of a polyurethane foam under compression during an in situ test

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In dieser Arbeit werden spektral codierte multispektrale Lichtfelder, wie sie von einer Lichtfeldkamera mit einem spektral codierten Mikrolinsenarray aufgenommen werden, untersucht. Für die Rekonstruktion der codierten Lichtfelder werden zwei Methoden entwickelt und im Detail ausgewertet. Zunächst wird eine vollständige Rekonstruktion des spektralen Lichtfelds entwickelt, die auf den Prinzipien des Compressed Sensing basiert. Um die spektralen Lichtfelder spärlich darzustellen, werden 5D-DCT-Basen sowie ein Ansatz zum Lernen eines Dictionary untersucht. Der konventionelle vektorisierte Dictionary-Lernansatz wird auf eine tensorielle Notation verallgemeinert, um das Lichtfeld-Dictionary tensoriell zu faktorisieren. Aufgrund der reduzierten Anzahl von zu lernenden Parametern ermöglicht dieser Ansatz größere effektive Atomgrößen. Zweitens wird eine auf Deep Learning basierende Rekonstruktion der spektralen Zentralansicht und der zugehörigen Disparitätskarte aus dem codierten Lichtfeld entwickelt. Dabei wird die gewünschte Information direkt aus den codierten Messungen geschätzt. Es werden verschiedene Strategien des entsprechenden Multi-Task-Trainings verglichen. Um die Qualität der Rekonstruktion weiter zu verbessern, wird eine neuartige Methode zur Einbeziehung von Hilfslossfunktionen auf der Grundlage ihrer jeweiligen normalisierten Gradientenähnlichkeit entwickelt und gezeigt, dass sie bisherige adaptive Methoden übertrifft. Um die verschiedenen Rekonstruktionsansätze zu trainieren und zu bewerten, werden zwei Datensätze erstellt. Zunächst wird ein großer synthetischer spektraler Lichtfelddatensatz mit verfügbarer Disparität Ground Truth unter Verwendung eines Raytracers erstellt. Dieser Datensatz, der etwa 100k spektrale Lichtfelder mit dazugehöriger Disparität enthält, wird in einen Trainings-, Validierungs- und Testdatensatz aufgeteilt. Um die Qualität weiter zu bewerten, werden sieben handgefertigte Szenen, so genannte Datensatz-Challenges, erstellt. Schließlich wird ein realer spektraler Lichtfelddatensatz mit einer speziell angefertigten spektralen Lichtfeldreferenzkamera aufgenommen. Die radiometrische und geometrische Kalibrierung der Kamera wird im Detail besprochen. Anhand der neuen Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail bewertet. Es werden verschiedene Codierungsmasken untersucht -- zufällige, reguläre, sowie Ende-zu-Ende optimierte Codierungsmasken, die mit einer neuartigen differenzierbaren fraktalen Generierung erzeugt werden. Darüber hinaus werden weitere Untersuchungen durchgeführt, zum Beispiel bezüglich der Abhängigkeit von Rauschen, der Winkelauflösung oder Tiefe. Insgesamt sind die Ergebnisse überzeugend und zeigen eine hohe Rekonstruktionsqualität. Die Deep-Learning-basierte Rekonstruktion, insbesondere wenn sie mit adaptiven Multitasking- und Hilfslossstrategien trainiert wird, übertrifft die Compressed-Sensing-basierte Rekonstruktion mit anschließender Disparitätsschätzung nach dem Stand der Technik

    Multisensory integration: does haptics improve tumour delineation?

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    The ability to use touch in addition to vision when searching for anomalies and differences in texture is well known to be beneficial to human perception in general. The aim of this thesis is to evaluate the potential benefit of using a haptic signal in conjunction with visual images to improve detection and delineation of tumours in medical imaging data. One of the key issues with tumour delineation in the field today is the interclinician variance in delineating tumours for diagnostics and treatment, where even clinicians who have similar sensitivity and precision levels tend to delineate widely different underlying shapes. Through three experiments we investigate whether the ability to touch a medical image improves tumour delineation. In the first experiment, we show that combined visuohaptic cues significantly improves performance for signal detection of a 2D Gaussian embedded in a noisy background. In the second experiment, we found that the relative dissimilarity of different images per modality did not systematically decrease precision in a two-alternative forced choice (2AFC) slant discrimination task, in a spatially coaligned visuohaptic rig. In the third and final experiment we successfully found that observers are significantly better at delineating generated ‘tumours’ in synthetic ‘medical images’ when the haptic representation of the image is present compared to drawing on a flat surface, in a spatially coaligned visuohaptic rig
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