Dissertations / Theses on the topic 'Motion reconstruction'

Sensing the real-world is a well-established and continual problem in the field of robotics. Investigations into autonomous aerial and underwater vehicles have extended this challenge into sensing, mapping and localising in three dimensions. This thesis seeks to understand and tackle the challenges of recovering 3D information from an environment using vision alone. There is a well-established literature on the principles of doing this, and some impressive demonstrations; but this thesis explores the practicality of doing vision-based 3D reconstruction using multiple, mobile robotic platforms, the emphasis being on producing accurate 3D models. Typically, robotic platforms such as UAVs have a single on-board camera, restricting which method of visual 3D recovery can be employed. This thesis specifically explores Structure from Motion, a monocular 3D reconstruction technique which produces detailed and accurate, although slow to calculate, 3D reconstructions. It examines how well proof-of-concept demonstrations translate onto the kinds of robotic systems that are commonly deployed in the real world, where local processing is limited and network links have restricted capacity. In order to produce accurate 3D models, it is necessary to use high-resolution imagery, and the difficulties of working with this on remote robotic platforms is explored in some detail.

In this thesis, a block-based motion estimation algorithm suitable for Super-Resolution (SR) image reconstruction is introduced. The motion estimation problem is formulated as an energy minimization problem that consists of both a data and regularization term. To handle cases when motion estimation fails, a block-based validity method is introduced, and is shown to outperform all other validity methods in the literature in terms of hybrid de-interlacing. By combining the validity metric into the energy minimization framework, it is shown that 1) the motion vector error is made less sensitive to block size, 2) a more uniform distribution of motion-compensated blocks results, and 3) the overall motion vector error is reduced. The final motion estimation algorithm is shown to outperform several state-of-the-art motion estimation algorithms in terms of both endpoint error and interpolation error, and is one of the fastest algorithms in the Middlebury benchmark. With the new motion estimation algorithm and validity metric, it is shown that artifacts are virtually eliminated from the POCS-based reconstruction of the high-resolution image.

L'objectif de cette thèse sont les corrections liées aux problèmes des mouvements respiratoires en tomographie d'émission. Il a été prouve que les mouvements respiratoires produisent des images floues, ce qui affecte la détection des lésions, les diagnostics, les traitements, etc. La solution proposée a été conçue pour opérer sans aucun dispositif externe. Cette méthode présente un schéma de la correction du mouvement basée sur un modèle inclus dans la reconstruction d'image. Le modèle prend en compte les déplacements et déformations des éléments d'émissions (voxels), lequel permet de considérer les déformations non rigides produites dans le thorax pendant la respiration. De plus, le model de voxel choisit, permet une amélioration aux calculs par rapport aux méthodes classiques. Deux models d'estimation etaitent développes. Un premier model simplifie consiste a adapter un model de respiration connu sur l'anatomie du patient. Le model initial décrit a travers un champ de déplacement les déformations du poumon produit entre les états de respiration extreme. Ce champ de déplacement est ensuite adapte sur l'anatomie du patient. La deuxième méthode a été conçu pour prendre en compte le manque de robustesse provoque par l'utilisation d'un seul sujet quand on construit les champs de déplacement connus. Incorporation de la variation des sujets dans un model statistique de respiration a été développe. La méthodologie a été développe dans un cadre de reconstruction d'image 3D et a été teste avec des données simules et réels
This thesis work deals with the problem of respiratory motion correction in emission tomography. It has been proven that respiratory motion renders blurred reconstructed images, affecting lesions detection, diagnosis, treatment, etc. The proposed approach was designed to work without any external tracking devices. It presents a retrospective scheme of motion correction based on a motion model plugged to the image reconstruction step. The model takes into account displacements and elastic deformations of emission elements (voxels), which allows to consider the non-rigid deformations produced in the thorax during respiration. Furthermore, the chosen voxel modeling improves computations, outperforming classical methods of voxel/detector-tube. Two estimation models were investigated and developed. A first simplified model consists on adapting a known respiratory motion model, obtained from a single subject, to the patient anatomy. The initial known model describes by means of a displacement vector field, the lungs deformations produced between extremal respiratory states. This displacement vector field is further adapted by means of an affine transformation to the patient's anatomy, yielding a displacement vector field that matches the thoracic cavity of the patient. The second method deals with the possible lack of robustness caused by the fact of using a single subject when constructing the known displacement vector field of the simplified method. Incorporation of subject variability into a statistical respiratory motion model was developed. The whole methodology was developed under a 3D framework and tested against simulated and real data

L’analisi del movimento umano ha come obiettivo la descrizione del movimento assoluto e relativo dei segmenti ossei del soggetto e, ove richiesto, dei relativi tessuti molli durante l’esecuzione di esercizi fisici. La bioingegneria mette a disposizione dell’analisi del movimento gli strumenti ed i metodi necessari per una valutazione quantitativa di efficacia, funzione e/o qualità del movimento umano, consentendo al clinico l’analisi di aspetti non individuabili con gli esami tradizionali. Tali valutazioni possono essere di ausilio all’analisi clinica di pazienti e, specialmente con riferimento a problemi ortopedici, richiedono una elevata accuratezza e precisione perché il loro uso sia valido. Il miglioramento della affidabilità dell’analisi del movimento ha quindi un impatto positivo sia sulla metodologia utilizzata, sia sulle ricadute cliniche della stessa. Per perseguire gli obiettivi scientifici descritti, è necessario effettuare una stima precisa ed accurata della posizione e orientamento nello spazio dei segmenti ossei in esame durante l’esecuzione di un qualsiasi atto motorio. Tale descrizione può essere ottenuta mediante la definizione di un modello della porzione del corpo sotto analisi e la misura di due tipi di informazione: una relativa al movimento ed una alla morfologia. L’obiettivo è quindi stimare il vettore posizione e la matrice di orientamento necessari a descrivere la collocazione nello spazio virtuale 3D di un osso utilizzando le posizioni di punti, definiti sulla superficie cutanea ottenute attraverso la stereofotogrammetria. Le traiettorie dei marker, così ottenute, vengono utilizzate per la ricostruzione della posizione e dell’orientamento istantaneo di un sistema di assi solidale con il segmento sotto esame (sistema tecnico) (Cappozzo et al. 2005). Tali traiettorie e conseguentemente i sistemi tecnici, sono affetti da due tipi di errore, uno associato allo strumento di misura e l’altro associato alla presenza di tessuti molli interposti tra osso e cute. La propagazione di quest’ultimo ai risultati finali è molto più distruttiva rispetto a quella dell’errore strumentale che è facilmente minimizzabile attraverso semplici tecniche di filtraggio (Chiari et al. 2005). In letteratura è stato evidenziato che l’errore dovuto alla deformabilità dei tessuti molli durante l’analisi del movimento umano provoca inaccuratezze tali da mettere a rischio l’utilizzabilità dei risultati. A tal proposito Andriacchi scrive: “attualmente, uno dei fattori critici che rallentano il progresso negli studi del movimento umano è la misura del movimento scheletrico partendo dai marcatori posti sulla cute” (Andriacchi et al. 2000). Relativamente alla morfologia, essa può essere acquisita, ad esempio, attraverso l’utilizzazione di tecniche per bioimmagini. Queste vengono fornite con riferimento a sistemi di assi locali in generale diversi dai sistemi tecnici. Per integrare i dati relativi al movimento con i dati morfologici occorre determinare l’operatore che consente la trasformazione tra questi due sistemi di assi (matrice di registrazione) e di conseguenza è fondamentale l’individuazione di particolari terne di riferimento, dette terne anatomiche. L’identificazione di queste terne richiede la localizzazione sul segmento osseo di particolari punti notevoli, detti repere anatomici, rispetto ad un sistema di riferimento solidale con l’osso sotto esame. Tale operazione prende il nome di calibrazione anatomica. Nella maggior parte dei laboratori di analisi del movimento viene implementata una calibrazione anatomica a “bassa risoluzione” che prevede la descrizione della morfologia dell’osso a partire dall’informazione relativa alla posizione di alcuni repere corrispondenti a prominenze ossee individuabili tramite palpazione. Attraverso la stereofotogrammetria è quindi possibile registrare la posizione di questi repere rispetto ad un sistema tecnico. Un diverso approccio di calibrazione anatomica può essere realizzato avvalendosi delle tecniche ad “alta risoluzione”, ovvero attraverso l’uso di bioimmagini. In questo caso è necessario disporre di una rappresentazione digitale dell’osso in un sistema di riferimento morfologico e localizzare i repere d’interesse attraverso palpazione in ambiente virtuale (Benedetti et al. 1994 ; Van Sint Jan et al. 2002; Van Sint Jan et al. 2003). Un simile approccio è difficilmente applicabile nella maggior parte dei laboratori di analisi del movimento, in quanto normalmente non si dispone della strumentazione necessaria per ottenere le bioimmagini; inoltre è noto che tale strumentazione in alcuni casi può essere invasiva. Per entrambe le calibrazioni anatomiche rimane da tenere in considerazione che, generalmente, i repere anatomici sono dei punti definiti arbitrariamente all’interno di un’area più vasta e irregolare che i manuali di anatomia definiscono essere il repere anatomico. L’identificazione dei repere attraverso una loro descrizione verbale è quindi povera in precisione e la difficoltà nella loro identificazione tramite palpazione manuale, a causa della presenza dei tessuti molli interposti, genera errori sia in precisione che in accuratezza. Tali errori si propagano alla stima della cinematica e della dinamica articolare (Ramakrishnan et al. 1991; Della Croce et al. 1999). Della Croce (Della Croce et al. 1999) ha inoltre evidenziato che gli errori che influenzano la collocazione nello spazio delle terne anatomiche non dipendono soltanto dalla precisione con cui vengono identificati i repere anatomici, ma anche dalle regole che si utilizzano per definire le terne. E’ infine necessario evidenziare che la palpazione manuale richiede tempo e può essere effettuata esclusivamente da personale altamente specializzato, risultando quindi molto onerosa (Simon 2004). La presente tesi prende lo spunto dai problemi sopra elencati e ha come obiettivo quello di migliorare la qualità delle informazioni necessarie alla ricostruzione della cinematica 3D dei segmenti ossei in esame affrontando i problemi posti dall’artefatto di tessuto molle e le limitazioni intrinseche nelle attuali procedure di calibrazione anatomica. I problemi sono stati affrontati sia mediante procedure di elaborazione dei dati, sia apportando modifiche ai protocolli sperimentali che consentano di conseguire tale obiettivo. Per quanto riguarda l’artefatto da tessuto molle, si è affrontato l’obiettivo di sviluppare un metodo di stima che fosse specifico per il soggetto e per l’atto motorio in esame e, conseguentemente, di elaborare un metodo che ne consentisse la minimizzazione. Il metodo di stima è non invasivo, non impone restrizione al movimento dei tessuti molli, utilizza la sola misura stereofotogrammetrica ed è basato sul principio della media correlata. Le prestazioni del metodo sono state valutate su dati ottenuti mediante una misura 3D stereofotogrammetrica e fluoroscopica sincrona (Stagni et al. 2005), (Stagni et al. 2005). La coerenza dei risultati raggiunti attraverso i due differenti metodi permette di considerare ragionevoli le stime dell’artefatto ottenute con il nuovo metodo. Tale metodo fornisce informazioni sull’artefatto di pelle in differenti porzioni della coscia del soggetto e durante diversi compiti motori, può quindi essere utilizzato come base per un piazzamento ottimo dei marcatori. Lo si è quindi utilizzato come punto di partenza per elaborare un metodo di compensazione dell’errore dovuto all’artefatto di pelle che lo modella come combinazione lineare degli angoli articolari di anca e ginocchio. Il metodo di compensazione è stato validato attraverso una procedura di simulazione sviluppata ad-hoc. Relativamente alla calibrazione anatomica si è ritenuto prioritario affrontare il problema associato all’identificazione dei repere anatomici perseguendo i seguenti obiettivi: 1. migliorare la precisione nell’identificazione dei repere e, di conseguenza, la ripetibilità dell’identificazione delle terne anatomiche e della cinematica articolare, 2. diminuire il tempo richiesto, 3. permettere che la procedura di identificazione possa essere eseguita anche da personale non specializzato. Il perseguimento di tali obiettivi ha portato alla implementazione dei seguenti metodi: • Inizialmente è stata sviluppata una procedura di palpazione virtuale automatica. Dato un osso digitale, la procedura identifica automaticamente i punti di repere più significativi, nella maniera più precisa possibile e senza l'ausilio di un operatore esperto, sulla base delle informazioni ricavabili da un osso digitale di riferimento (template), preliminarmente palpato manualmente. • E’ stato poi condotto uno studio volto ad indagare i fattori metodologici che influenzano le prestazioni del metodo funzionale nell’individuazione del centro articolare d’anca, come prerequisito fondamentale per migliorare la procedura di calibrazione anatomica. A tale scopo sono stati confrontati diversi algoritmi, diversi cluster di marcatori ed è stata valutata la prestazione del metodo in presenza di compensazione dell’artefatto di pelle. • E’stato infine proposto un metodo alternativo di calibrazione anatomica basato sull’individuazione di un insieme di punti non etichettati, giacenti sulla superficie dell’osso e ricostruiti rispetto ad un TF (UP-CAST). A partire dalla posizione di questi punti, misurati su pelvi coscia e gamba, la morfologia del relativo segmento osseo è stata stimata senza identificare i repere, bensì effettuando un’operazione di matching dei punti misurati con un modello digitale dell’osso in esame. La procedura di individuazione dei punti è stata eseguita da personale non specializzato nell’individuazione dei repere anatomici. Ai soggetti in esame è stato richiesto di effettuare dei cicli di cammino in modo tale da poter indagare gli effetti della nuova procedura di calibrazione anatomica sulla determinazione della cinematica articolare. I risultati ottenuti hanno mostrato, per quel che riguarda la identificazione dei repere, che il metodo proposto migliora sia la precisione inter- che intraoperatore, rispetto alla palpazione convenzionale (Della Croce et al. 1999). E’ stato inoltre riscontrato un notevole miglioramento, rispetto ad altri protocolli (Charlton et al. 2004; Schwartz et al. 2004), nella ripetibilità della cinematica 3D di anca e ginocchio. Bisogna inoltre evidenziare che il protocollo è stato applicato da operatori non specializzati nell’identificazione dei repere anatomici. Grazie a questo miglioramento, la presenza di diversi operatori nel laboratorio non genera una riduzione di ripetibilità. Infine, il tempo richiesto per la procedura è drasticamente diminuito. Per una analisi che include la pelvi e i due arti inferiori, ad esempio, l’identificazione dei 16 repere caratteristici usando la calibrazione convenzionale richiede circa 15 minuti, mentre col nuovo metodo tra i 5 e i 10 minuti.

Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains including archaeology, oil and gas industry, coral reef monitoring, harbour’s security, and mine countermeasure missions. As electromagnetic signals do not penetrate underwater environment, GPS signals cannot be used for AUV navigation, and optical cameras have very short range underwater which limits their use in most underwater environments. Motion estimation for AUVs is a critical requirement for successful vehicle recovery and meaningful data collection. Classical inertial sensors, usually used for AUV motion estimation, suffer from large drift error. On the other hand, accurate inertial sensors are very expensive which limits their deployment to costly AUVs. Furthermore, acoustic positioning systems (APS) used for AUV navigation require costly installation and calibration. Moreover, they have poor performance in terms of the inferred resolution. Underwater 3D imaging is another challenge in AUV industry as 3D information is increasingly demanded to accomplish different AUV missions. Different systems have been proposed for underwater 3D imaging, such as planar-array sonar and T-configured 3D sonar. While the former features good resolution in general, it is very expensive and requires huge computational power, the later is cheaper implementation but requires long time for full 3D scan even in short ranges. In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by proposing relatively affordable methodologies and study different parameters affecting their performance. We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on forward looking sonars; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars.

Magnetic Resonance Imaging (MRI) is a powerful imaging modality with excellent soft tissue contrast and high spatial resolution without the need for ionising radiation. However, the acquisition process is inherently slow, which imposes practical constraints on the modality. Scan times are particularly long in three-dimensional high spatial resolution imaging. This diculty has recently been alleviated by accelerated acquisitions, combined with Parallel Imaging or Compressed Sensing reconstructions. Patient motion is one of the major obstacles in clinical MRI, as physiological motion is typically faster than the acquisition process. Motion occurring during a scan will corrupt the acquired data and introduce image artefacts in the reconstructed image. Unavoidable types of motion such as respiratory motion must be considered for a successful MR examination. The problem of respiratory motion is most predominant in abdominal and cardiac imaging. To tackle this concern, motion corrupted data is commonly rejected using the so-called gated data acquisition. However, scan times are increased further as rejected data needs to be re-acquired. A more ecient approach to this problem is to acquire motion corrupted data and attempt to correct this data afterwards. Novel approaches for respiratory motion correction are developed in this thesis. The proposed framework estimates complex, non-rigid motion from the data itself. The motion information is then incorporated into the reconstruction to remove motion-related artefacts. This non-rigid motion correction framework is adapted to three different applications: 3D accelerated abdominal imaging, 3D coronary lumen and vessel wall imaging, and 3D whole-heart water/fat imaging. In the rst application, the framework is combined with Parallel Imaging and Compressed Sensing to enable high acceleration factors. The proposed method reduced scan times by 2.6x when compared with the gated acquisition while maintaining similar image quality. In the second application, the framework is combined with interleaved image navigators to add high temporal resolution motion correction. This method also presented similar coronary lumen quality to the gated, despite a 1.6x reduction in scan time. Additionally, it presented signi cantly superior vessel wall quality when compared to translation correction. In the third application, the framework is combined with Parallel Imaging, Compressed Sensing and interleaved image navigators. Initial results indicate the proposed approach produces signi - cantly superior water and fat images than translation correction.

This thesis covers two main areas which are related to the mapping and examination of the ionosphere. The first examines the performance and specific nuances of various state-of-the-art interpolation methods with specific application to mapping the ionosphere. This work forms the most widely scoped examination of interpolation technique for ionospheric imaging to date, and includes the introduction of normalised convolution techniques to geophysical data. In this study, adaptive-normalised convolution was found to perform well in ionospheric electron content mapping, and the popular technique, kriging was found to have problems which limit its usefulness. The second, is the development and examination of automatic data-driven motion estimation methods for use on ionospheric electron content data. Particular emphasis is given to storm events, during which characteristic shapes appear and move across the North Pole. This is a particular challenge, as images covering this region tend to have a very-low resolution. Several motion estimation methods are developed and applied to such data, including methods based on optical flow, correlation and boundarycorrespondence. Correlation and relaxation labelling based methods are found to perform reasonably, and boundary based methods based on shape-context matching are found to perform well, when coupled with a regularisation stage. Overall, the techniques examined and developed here will help advance the process of examining the features and morphology of the ionosphere, both during storms and quiet times.

Single view reconstruction is fundamentally an under-constrained problem. We aim to develop new approaches to model human face and motion with model priors that restrict the space of possible solutions. First, we develop a novel approach to recover the 3D shape from a single view image under challenging conditions, such as large variations in illumination and pose. The problem is addressed by employing the techniques of non-linear manifold embedding and alignment. Specifically, the local image models for each patch of facial images and the local surface models for each patch of 3D shape are learned using a non-linear dimensionality reduction technique, and the correspondences between these local models are then learned by a manifold alignment method. Local models successfully remove the dependency of large training databases for human face modeling. By combining the local shapes, the global shape of a face can be reconstructed directly from a single linear system of equations via least square. Unfortunately, this learning-based approach cannot be successfully applied to the problem of human motion modeling due to the internal and external variations in single view video-based marker-less motion capture. Therefore, we introduce a new model-based approach for capturing human motion using a stream of depth images from a single depth sensor. While a depth sensor provides metric 3D information, using a single sensor, instead of a camera array, results in a view-dependent and incomplete measurement of object motion. We develop a novel two-stage template fitting algorithm that is invariant to subject size and view-point variations, and robust to occlusions. Starting from a known pose, our algorithm first estimates a body configuration through temporal registration, which is used to search the template motion database for a best match. The best match body configuration as well as its corresponding surface mesh model are deformed to fit the input depth map, filling in the part that is occluded from the input and compensating for differences in pose and body-size between the input image and the template. Our approach does not require any makers, user-interaction, or appearance-based tracking. Experiments show that our approaches can achieve good modeling results for human face and motion, and are capable of dealing with variety of challenges in single view reconstruction, e.g., occlusion.

Computed tomography (CT), and especially cone-beam computed tomography (CBCT) has a wide range of applications. This thesis focuses on CBCT for image-guided radiation therapy (IGRT), particularly for lung cancer treatment. In lung IGRT the tumour moves due to respiration, not only making it hard to target with the radiation beam, but also blurring the images acquired for daily treatment tuning. Generating high quality images without motion artefacts is essential for {radiation} and hadron therapy. In this thesis, motion modelling ideas from CERN's phase space tomography are modified and adapted to lung CBCT. The CERN method includes a knowledge of the motion in the basic building blocks of the image reconstruction and uses all the acquired data to reconstruct a single static image at any chosen moment within the acquisition timespan. In order to use this method, and in general improve the reconstructed image quality of CBCT, iterative algorithms are explored with a focus on fast reconstruction using GPUs. The work presented here lead to the publication of the TIGRE Toolbox, a fast, easy-to-use MATLAB-CUDA toolbox for the reconstruction of CBCT images at state-of-the-art speeds with an extensive variety of iterative algorithms. This thesis presents the mathematics, GPU techniques and different applications of TIGRE and its algorithms, strengthening the idea already stated that iterative algorithms can significantly improve image quality in CBCT. A motion compensation method is developed together with a fast GPU implementation and its robustness is tested numerically by simulating the expected clinical errors in the data. The method is very robust and provides high-quality static images using data from disparate moments in time, offering the prospect of videos of patients breathing at no extra cost in radiation dose.

Medical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, '1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 55-57).
Sub-pixel movement detection is an under-sampling problem. The basic idea for successful detection is to spread out the information over a larger sampling region. Diffraction provides a natural way to spread out the information; however, conventional digital holographic methods are not effective for extracting sub-pixel accuracy. Here we show how to apply compressive reconstruction to the same problem effectively. Compressed sensing is a new framework to systematically find highly accurate solutions to an under-sampled linear system. To guarantee the accuracy of reconstruction result, compressed sensing requires that the unknown has to be sparse in some predetermined basis. In our study, for the one dimensional sub-pixel movement detection, we propose to use the derivative operator as the sparsifying basis. We implemented the derivative operator to the hologram and applied a sparsity constraint on the object derivative space for compressive holography. Together with spectrum domain zero-padding, our compressive algorithm allows for sub-pixel accuracy edge localization. The extension to the 2D case is not trivial. It has been shown that the spiral phase mask can serve as an approximate 2D derivative operator in the Fourier domain. In this case, we implemented spiral phase filtering in the hologram spectrum domain. By applying cross-correlation between reconstructions for consecutive subpixel movements, sub-pixel movement was successfully detected.
by Yi Liu.
S.M.

When patients move during an MRI examination, severe artifacts arise in the reconstructed image and motion correction is therefore often desired. An in-plane motion correction algorithm suitable for PRESTO-CAN, a new 3D functional MRI method where sampling of k-space is radial in kx-direction and kz-direction and Cartesian in ky-direction, was implemented in this thesis work. Rotation and translation movements can be estimated and corrected for sepa- rately since the magnitude of the data is only affected by the rotation. The data were sampled in a radial pattern and the rotation was estimated by finding the translation in angular direction using circular correlation. Correlation was also used when finding the translation in x-direction and z-direction. The motion correction algorithm was evaluated on computer simulated data, the motion was detected and corrected for, and this resulted in images with greatly reduced artifacts due to patient movements.
När patienter rör sig under en MRI-undersökning uppstår artefakter i den rekonstruerande bilden och därför är det önskvärt med rörelsekorrigering. En 2D- rörelsekorrigeringsalgoritm som är anpassad för PRESTO-CAN har tagits fram. PRESTO-CAN är en ny fMRI-metod för 3D där samplingen av k-rummet är radiell i (kx,kz)-planet och kartesisk i ky-riktningen. Rotations- och translationsrörelser kan estimeras separat då magnituden av signalen bara påverkas av rotationsrörelser. Eftersom data är samplat radiellt kan rotationen estimeras genom att hitta translationen i vinkelled med hjälp av cirkulär korrelation. Korrelation används även för att hitta translationen i i x- och z-riktningen. Test på simulerat data visar att rörelsekorrigeringsalgoritmen både detekterar och korrigerar för rörelser vilket leder till bilder med mycket mindre rörelseartefakter.

In a roofing project, acquiring the underlying as-built 3D geometry and visualizing the roof structure is needed in different phases of the project life-cycle. Architectural drawings, building information model (BIM) files, or aerial photogrammetry are used to estimate the roofing area in the bidding process. However, as a roof structure is never built to the exact drawing dimensions, as-built dimensions of boundaries of every roof plane have to be obtained several times during the course of its build. There are a number of surveying methods that can be used for this purpose: tape measuring, total station surveying, aerial photogrammetry, and laser scanning. However, obtaining measurements using these methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. Aiming to address this limitation and provide roofing practitioners with an alternative roof surveying and visualization method that is simple to use, automated, inexpensive, and safe, a close-range videogrammetric roof 3D reconstruction framework is presented in this research. When using this method, a roofing contractor will simply collect stereo video streams of a target roof. The captured data is processed to generate a 3D wire-diagram for every roof plane. In this process, distinctive visual features of the scene (e.g., 2D points and lines) are first automatically detected and matched between video frames. Matched features and the camera calibration information are used to compute an initial estimation of the 3D structure. Then, a hybrid bundle adjustment algorithm is used to refine the result and acquire the geometry that has the maximum likelihood. Afterwards, different roof planes are found and a measurable 3D wire-diagram is generated for each plane.

This master thesis investigates the difficulties of constructing a depth map using one low resolution grayscale camera mounted in the front of a car. The goal is to produce a depth map in real-time to assist other algorithms in the safety system of a car. This has been shown to be difficult using the evaluated combination of camera position and choice of algorithms. The main problem is to estimate an accurate optical flow. Another problem is to handle moving objects. The conclusion is that the implementations, mainly triangulation of corresponding points tracked using a Lucas Kanade tracker, provide information of too poor quality to be useful for the safety system of a car.
I detta examensarbete undersöks svårigheterna kring att skapa en djupbild från att endast använda en lågupplöst gråskalekamera monterad framtill i en bil. Målet är att producera en djupbild i realtid som kan nyttjas i andra delar av bilens säkerhetssystem. Detta har visat sig vara svårt att lösa med den undersökta kombinationen av kameraplacering och val av algoritmer. Det huvudsakliga problemet är att räkna ut ett noggrant optiskt flöde. Andra problem härrör från objekt som rör på sig. Slutsatsen är att implementationerna, mestadels triangulering av korresponderande punktpar som följts med hjälp av en Lucas Kanade-följare, ger resultat av för dålig kvalitet för att vara till nytta för bilens säkerhetssystem.

Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2009.
Engineers struggle to replicate the capabilities of the sophisticated human visual system. This thesis sets out to recover motion and 3D structure of multiple rigid objects up to a similarity. The motion of these objects are either recorded in a single video sequence, or images of the objects are recorded on multiple, di erent cameras. We assume a perspective camera model with optional provision for calibration information. The Structure from Motion (SfM) problem is addressed from a matrix factorization point of view. This leads to a reconstruction correct up to a projectivity of little use in itself. Using techniques from camera autocalibration the projectivity is upgraded to a similarity. This reconstruction is also applied to multiple objects through motion segmentation. The SfM system developed in this thesis is a batch-processing algorithm, requiring few frames for a solution and readily accepts images from very di erent viewpoints. Since a solution can be obtained with just a few frames, it can be used to initialize sequential methods with slower convergence rates, such as the Kalman lter. The SfM system is critically evaluated against an extensive set of motion sequences.

This thesis is an examination of British films which discuss and propose the reconstruction of the built enviromnent. It concentrates on the period 1939-51 but also looks at those films made during the inter-war period. It examines how and why the films were produced, and how they present the issues of reconstruction. The particular aims are to see what the films might tell us about the relationship between planners, architects, politicians and the ordinary people - the people who would be the beneficiaries of reconstruction. Secondly, to ascertain what impact the films had on popular attitudes to town planning and building. The main findings are that the films were considered a very important way of communicating with the general public and that they were specifically designed to widen the debate and the process of reconstruction beyond the professionals to ordinary citizens. However, despite these noble and sincere aims the films had only limited effect in achieving this. As a result of studying the production and distribution of the films one has also a better understanding of the relationship between film-makers and the government propaganda agencies to which they were contracted. The most important conclusion from this aspect of the research is that they were highly constrained in the kind of films on reconstruction they could make despite their efforts to produce radical work. Finally the Central Office of Information films of the post-war period show that the Labour Government was similarly committed to involving and informing the people in the new world that they planned to build.

The primary goal of this thesis is to develop generic motion and structure algorithms for images taken from constructed scenes by various types of central imaging systems including perspective, fish-eye and catadioptric systems. As-suming that the mapping between the image pixels and their 3D rays in space is known, instead of image planes, we work on image spheres (projection of the images on a unit sphere) which enable us to present points over the entire viewsphere suitable for presenting omnidirectional images. In the first part of this thesis, we develop a generic and simple line matching approach for images taken from constructed scenes under a short baseline motion as well as a fast and original geometric constraint for matching lines in planar constructed scenes insensible to the motion of the camera for all types of centralimages including omnidirectional images.Next, we introduce a unique and efficient way of computing overlap between two segments on perspective images which considerably decreases the over all computational time of a segment-based motion estimation and reconstruction algorithm. Finally in last part of this thesis, we develop a simple motion estima-tion and surface reconstruction algorithm for piecewise planar scenes applicable to all kinds of central images which uses only two images and is based on mini-mum line correspondences.To demonstrate the performance of these algorithms we experiment withvarious real images taken by a simple perspective camera, a fish-eye lens, and two different kinds of paracatadioptric sensors, the first one is a folded catadioptric camera and the second one is a classic paracatadioptric system composed of a parabolic mirror in front of a telecentric lens.

Recently, significant advances have been made in many sub-areas regarding the problem of markerless human motion capture. However, markerless solutions still tend to introduce major simplifications, especially in early stages of the process, that temper the robustness and the generality of any subsequent modules and, consequently, of the whole application. This thesis concentrates on improving the aspects of multi-camera system design, multi-camera calibration and shape-from-silhouette volumetric reconstruction. In Chapter 3, a thoughtful system analysis is first proposed with the objective of achieving an optimal synchronized multi-camera system. Chapter 4 proposes an easy-to-use multi-camera calibration technique to estimate the relative positioning and orientation of every camera with sub-pixel accuracy. In Chapter 5 a robust shape-from-silhouette algorithm, with precise voxel coloring, is developed. Overall, the proposed framework is successful to reconstruct various 3D human postures and, in particular, complex and self-occlusive pianist postures in real-world (minimally constrained) scenes.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Alvo de intenso estudo da visão computacional, a reconstrução densa 3D teve um importante marco com os primeiros sistemas em tempo real a alcançarem precisão milimétrica com uso de câmeras RGBD e GPUs. Entretanto estes métodos não são aplicáveis a dispositivos de menor poder computacional. Tendo a limitação de recursos computacionais como requisito, o objetivo deste trabalho é apresentar um método de odometria visual utilizando câmeras comuns e sem a necessidade de GPU, baseado em técnicas de Structure from Motion (SFM) com features esparsos, utilizando as informações de uma reconstrução densa. A Odometria visual é o processo de estimar a orientação e posição de um agente (um robô, por exemplo), a partir das imagens. Esta dissertação fornece uma comparação entre a precisão da odometria calculada pelo método proposto e pela reconstrução densa utilizando o Kinect Fusion. O resultado desta pesquisa é diretamente aplicável na área de realidade aumentada, tanto pelas informações da odometria que podem ser usadas para definir a posição de uma câmera, como pela reconstrução densa, que pode tratar aspectos como oclusão dos objetos virtuais com reais.
Aim of intense research in the field computational vision, dense 3D reconstruction achieves an important landmark with first methods running in real time with millimetric precision, using RGBD cameras and GPUs. However these methods are not suitable for low computational resources. Having low computational resources as requirement, the goal of this work is to show a method of visual odometry using regular cameras, without using a GPU. The proposed method is based on technics of sparse Structure From Motion (SFM), using data provided by dense 3D reconstruction. Visual odometry is the process of estimating the position and orientation of an agent (a robot, for instance), based on images. This dissertation compares the proposed method with the odometry calculated by Kinect Fusion. Results of this research are applicable in augmented reality. Odometry provided by this work can be used to model a camera and the data from dense 3D reconstruction, can be used to handle occlusion between virtual and real objects.

Realism in animation sequences requires movements to be adapted to changing environments within the virtual world. To enhance visual experiences from animated characters, research is being focused on recreating realistic character movement adapted to surrounding environment within the character's world. Existing methods as applied to the problem of controlling character animations are often poorly suited to the problem as they focus on modifying and adapting static sequences, favoring responsiveness and reaching the motion objective rather than realism in characters movements.   Algorithms for synthesizing motion sequences can then bridge the gap between motion quality and responsiveness, and recent methods have shown to open the possibility to recreate specific motions and movement patterns. Effectiveness of proposed methods to synthesize motion can however be questioned, particularly due to the sparsity and quality of evaluations between methods. An issue which is further complicated by variations in learning tasks and motion data used to train models.   Rather than directly propose a new synthesis method, focus is put on refuting existing methods by applying them to the task of synthesizing objective-oriented motion involving the action of throwing a ball. To achieve this goal, two experiments are designed. The first experiment evaluates if a phase-functioned neural network (PFNN) model based on absolute joint configurations can generate objective oriented motion.   To achieve this objective, a separate approach utilizing a hierarchy of phase-function networks is designed and implemented. By comparing application of the two methods on the learning task, the proposed hierarchy model showed significant improvement regarding the ability to fit generated motion to intended end effector trajectories.   To be able to refute the idea of using dense feed-forward neural networks, a second experiment is performed comparing PFNN and feed-forward based network hierarchies. Outcome from the experiment show significant differences in favor for the hierarchy model utilizing phase-function networks.   To facilitate experimentation, objective oriented motion data for training network models are obtained by researching and implementing methods for processing optical motion capture data over repeated practices of over-arm ball throws. Contribution is then threefold: creation of a dataset containing motion sequences of ball throwing actions, evaluation of PFNN on the task of learning sequences of objective oriented motion, and definition of a hierarchy based neural network model applicable to the motion synthesis task.

The main aim of this thesis is to generate 3D models of human heads from uncalibrated images. In order to extract geometric values of a human head, we find camera parameters using camera auto calibration. However, some image sequences generate non-unique (degenerate) solutions. An algorithm for removing degeneracy from the most common form of camera movement in face image acquisition is described. The geometric values of main facial features are computed initially. The model is then generated by gradual deformation of a generic polygonal model of a head. The accuracy of the models is evaluated using ground truth data from a range scanner. 3D models are covered with cylindrical texture values obtained from images. The models are appropriate for animation or identification applications.

Accurate motion capture of deformable objects from monocular video sequences is a challenging Computer Vision problem with immense applicability to domains ranging from virtual reality, animation to image guided surgery. Existing dense motion capture methods rely on expensive setups with multiple calibrated cameras,structured light, active markers or prior scene knowledge learned from a large 3D dataset. In this thesis, we propose an end-to-end pipeline for 3D reconstruction of deformable scenes from a monocular video sequence. Our method relies on a two step pipeline in which temporally consistent video registration is followed by a dense non-rigid structure from motion approach. We present a data-driven method to reconstruct non-rigid smooth surfaces densely, using only a single video as input, without the need for any prior models or shape templates. We focus on the well explored low-rank prior for deformable shape reconstruction and propose its convex relaxation to introduce the first variational energy minimisation approach to non-rigid structure from motion. To achieve realistic dense reconstruction of sparsely textured surfaces, we incorporate an edge preserving spatial smoothness prior into the low-rank factorisation framework and design a single variational energy to address the non-rigid structure from motion problem. We also discuss the importance of long-term 2D trajectories for several vision problems and explain how subspace constraints can be used to exploit the redundancy present in the motion of real scenes for dense video registration. To that end, we adopt a variational optimisation approach to design a robust multi-frame video registration algorithm that combines a robust subspace prior with a total variation spatial regulariser. Throughout this thesis, we advocate the use of GPU-portable and scalable energy minimisation algorithms to progress towards practical dense non-rigid 3D motion capture from a single video in the presence of occlusions and illumination changes.

Achieving robot autonomy is an extremely challenging task and it starts with developing algorithms that help the robot understand how humans perceive the environment around them. Once the robot understands how to make sense of its environment, it is easy to make efficient decisions about safe movement. It is hard for robots to perform tasks that come naturally to humans like understanding signboards, classifying traffic lights, planning path around dynamic obstacles, etc. In this work, we take up one such challenge of motion segmentation using Light Detection and Ranging (LiDAR) point clouds. Motion segmentation is the task of classifying a point as either moving or static. As the ego-vehicle moves along the road, it needs to detect moving cars with very high certainty as they are the areas of interest which provide cues to the ego-vehicle to plan it's motion. Motion segmentation algorithms segregate moving cars from static cars to give more importance to dynamic obstacles. In contrast to the usual LiDAR scan representations like range images and regular grid, this work uses a modern representation of LiDAR scans using permutohedral lattices. This representation gives ease of representing unstructured LiDAR points in an efficient lattice structure. We propose a machine learning approach to perform motion segmentation. The network architecture takes in two sequential point clouds and performs convolutions on them to estimate if 3D points from the first point cloud are moving or static. Using two temporal point clouds help the network in learning what features constitute motion. We have trained and tested our learning algorithm on the FlyingThings3D dataset and a modified KITTI dataset with simulated motion.

Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed June 23, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 129-150).

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts.

The recent emergence of Structure-from-Motion Photogrammetry (SfM) has created a cost-effective alternative to conventional laser scanning for the production of high-resolution topographic datasets. There has been an explosion of applications of SfM within the geomorphological community in recent years, however, the focus of these has largely been small-scale (102 - 103 m2), building on innovations in low altitude Unmanned Aircraft Systems (UAS). This thesis examines the potential to extend the scope of SfM photogrammetry in order to quantify of landscape scale processes. This is examined through repeat surveys of a ~35 km2 reach of the Dart River, New Zealand. An initial SfM survey of this reach was conducted in April 2014, following a large landslide at the Slipstream debris fan. Validation of the resulting digital elevation models using Independent Control Point's (ICPs) suggested encouraging results, however benchmarking the survey against a long-range laser scanned surface indicated the presence of significant systematic errors associated with inaccurate estimation of the SfM bundle adjustment. Using a combination of scaled laboratory field experiments, this research aimed to develop and test photogrammetric data collection and modelling strategies to enhance modelling of 3D scene structure using limited constraints. A repeat survey in 2015 provided an opportunity to evaluate a new survey strategy, incorporating a convergent camera network and a priori measurement of camera pose. This resulted in halving of mean checkpoint residuals and a reduction in systematic error. The models produced for both 2014 and 2015 were compared using a DEM differencing (DoD) methodology to assess the applicability of wide-area SfM models for the analysis of geomorphic change detection. The systematic errors within the 2014 model confound reliable change detection, although strategies to correlate the two surveys and measure the residual change show promise. The future use of SfM over broad landscape scales has significant potential, however, this will require robust data collection and modelling strategies and improved error modelling to increase user confidence.

This paper presents a new approach to the feature-matching problem for 3D reconstruction by taking advantage of GPS and IMU data, along with a prior calibrated stereo camera system. It is expected that pose estimates and calibration can be used to increase feature matching speed and accuracy. Given pose estimates of cameras and extracted features from images, the algorithm first enumerates feature matches based on stereo projection constraints in 2D and then backprojects them to 3D. Then, a grid search algorithm over potential camera poses is proposed to match the 3D features and find the largest group of 3D feature matches between pairs of stereo frames. This approach will provide pose accuracy to within the space that each grid region covers. Further refinement of relative camera poses is performed with an iteratively re-weighted least squares (IRLS) method in order to reject outliers in the 3D matches. The algorithm is shown to be capable of running in real-time correctly, where the majority of processing time is taken by feature extraction and description. The method is shown to outperform standard open source software for reconstruction from imagery.
Master of Science

Cone-beam computed tomography (CBCT) is an imaging technique used in conjunction with radiation therapy. CBCT is used to verify the position of tumours just prior to radiation treatment session. The accuracy of the radiation treatment of thoracic and upper abdominal tumours is heavily affected by respiratory movement. Blurring artefacts, due to the movement during a CBCT scanning, cause misregistration between the CBCT image and the planning image. There has been growing interest in the use of motion-compensated CBCT for correcting the breathing-induced artefacts. A wide range of iterative reconstruction methods have been developed for CBCT imaging. The direct motion compensation technique has been applied to algebraic reconstruction technique (ART), simultaneous ART (SART), ordered-subset SART (OS-SART) and conjugate gradient least squares (CGLS). In this thesis a dual modality imaging of electrical impedance tomography (EIT) and CBCT is proposed for the first time. This novel dual modality imaging uses the advantages of high temporal resolution of EIT imaging and high spatial resolution of the CBCT method. The main objective of this study is to combine CBCT with EIT imaging system for motion-compensated CBCT using experimental and computational phantoms. The EIT images were used for extracting motion for a motion-compensated CBCT imaging system. A simple motion extraction technique is used for extracting motion data from the low spatial resolution EIT images. This motion data is suitable for input into the direct motion-compensated CBCT. The performance of iterative algorithms for motion compensation was also studied. The dual modality CBCT-EIT is verified using experimental EIT system and computational CBCT phantom data.

L’estimation de mouvement de caméras et la reconstruction de structure 3D (Structure from Motion ou SfM) ont déjà beaucoup étudiées. Il existe de nombreuses méthodes de SfM qui diffèrent en fonction du type de capteurs et de primitives utilisés. La plupart de ces approches ont été développées pour la vision perspective ou omnidirectionnelle mais très peu pour les deux. Une combinaison de la bonne résolution fournie par les caméras perspectives et le large champ de vue des caméras omnidirectionnelles devient très utile dans plusieurs applications comme la surveillance, la reconstruction de scène, la navigation d’un robot mobile, etc. C’est la raison pour laquelle nous recherchons une méthode qui est applicable à ces deux systèmes de vision. Nous proposons une technique de SfM basée droites qui possèdent de nombreuses caractéristiques avantageuses, particulièrement en milieu urbain. Cette méthode se compose de l’estimation de mouvement en deux étapes (calcul des rotations à partir de points de fuite de droites parallèles dans la scène et calcul des translations à partir de ces mêmes droites), la reconstruction de structure 3D et l’ajustement de faisceaux afin de raffiner les paramètres de caméras et de structure 3D. De plus, les points peuvent être intégrés dans l’estimation de translations pour améliorer le résultat. Ainsi, nous avons développé dans cette thèse une approche originale qui permet d’estimer le mouvement et de reconstruire la scène avec tout type de caméras centrales ou fish-eye et en utilisant deux types de primitives (les droites et les points)
Structure from motion is a widely studied problem in computer vision. It refers to the estimation of camera motion and three-dimensional structure of the scene. There exist numerous solutions to structure from motion problems, varying in types of vision equipment, kinds of image feature and estimation process. Most of them coped with perspective or omnidirectional vision but very few with both. Combining the good resolution provided by perspective cameras and the wide field of view of omnidirectional ones has become an attractive trend. For this reason, we seek for an approach that is applicable to both of these vision sensors. We propose a structure from motion algorithm using lines as this feature possesses many advantageous characteristics over points, especially in urban environment. This method consists of linear motion estimation based on line correspondences, 3D reconstruction and bundle adjustment to refine the camera and structure parameters. Besides lines, points can be integrated in our translation estimation to improve its performance. The contributions of this thesis concern the applicability of the proposed method to any type of central projection cameras including fish-eye ones, the advantage of using line images, and the exploitation of both point and line features

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration.

Computational imaging becomes a cutting edge research area by incorporating signal/image processing as an inherent part of an imaging system. Its civil and military applications include surveillance, automobile, and medical health. The newest branch of computational imaging, compressive imaging emerged in several years back. In-stead of making measurement for each individual object pixel, compressive imaging directly making compressed measurements using optical/opto-electronic devices in data acquisition process. These compressed measurements referred to as features are linear combinations of object pixels weighted by transformation bases. Usingvarious types of signal processing techniques, features are processed for the imaging system final tasks such as reconstruction, detection, and recognition. In this dissertation, three compressive imaging implementation architectures, sequential, parallel, and photon-sharing architectures, are analyzed. Two kinds of applications, object reconstruction and motion detections, are studied using projections including PC (Principal Component), Hadamard, DCT (Discrete Cosine Transformation), Gabor, and random projection. Linear and/or nonlinear algorithms are used for static and adaptive measurements. A webcam based multi-sensor network and a DMD based single detector imaging system demonstrate the dissertation work.

Thesis (M.S.)--Texas A&M University, 2003.
"Major Subject: Electrical Engineering" Title from author supplied metadata (record created on Jul. 18, 2005.) Vita. Abstract. Includes bibliographical references.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.
Includes bibliographical references (p. 199-205).
Vortex-induced vibration (VIV) of long flexible cylindrical structures enduring ocean currents is ubiquitous in the offshore industry. Though significant effort has gone into understanding this complicated fluid-structure interaction problem, major challenges remain in modeling and predicting the response of such structures. The work presented in this thesis provides a systematic approach to estimate and analyze the vortex-induced motions and forces on a marine riser, and develop suitable methods to improve riser VIV modeling and response prediction. In the first part of the thesis, a systematic framework is developed, which allows reconstruction of the riser motion from a limited number of sensors placed along its length. A perfect reconstruction criterion is developed, which allows us to classify when the measurements from the sensors contain all information pertinent to VIV response, and when they do not, in which case additional, analytical methods must be employed. Reconstruction methods for both scenarios are developed and applied to experimental data. The methods are applied to: develop tools for in-situ estimation of fatigue damage on marine risers; improve understanding of the vortex shedding mechanisms, including the presence of traveling waves and higher-harmonic forces; and estimate the vortex-induced forces on marine risers. In the second part of the thesis, a method is developed to improve the modeling of riser VIV by extracting empirical lift coefficient databases from field riser VIV measurements. The existing experiment-based lift coefficient databases are represented in a flexible parameterized form using a set of carefully chosen parameters. Extraction of the lift coefficient parameters is posed as an optimization problem, where the error between the prediction using a theoretical model and the experimental data is minimized.
(cont.) Predictions using the new databases are found to significantly reduce the error in estimating the riser cross-flow response. Finally, data from a comprehensive experiment is utilized to show that the riser response is resonant in the harmonic component, but non-resonant in the third-harmonic component. It is shown that this happens because the spatial dependence of the third-harmonic fluid force component is dominated by the first-harmonic wavelengths. This finding has significant implications for modeling the higher-harmonic forces and the resulting fatigue damage estimation methodologies.
by Harish Mukundan.
Ph.D.

Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV through unknown environments are some of the factors that constraint fully automated UAV inspections. This work demonstrates the use of unsupervised Machine Learning methods to develop an algorithm capable of constructing a 3D model of an unknown environment in an autonomous iterative way. The capabilities of this novel approach are tested in a field study, where a municipal water tank is mapped to a level of resolution comparable to that of manual missions by experienced engineers but using $63\%$ . The iterative approach also shows improvements in autonomy and model coverage when compared to reproducible automated flights. Additionally, the use of this algorithm for different terrains is explored through simulation software, exposing the effectiveness of the automated iterative approach in other applications.

Phase Measuring Profilometry (PMP) is the most robust scanning technique for static 3D data acquisition. To make this technique robust to the target objects which are in motion during the scan interval a novel algorithm called ‘Pattern Interleaving’ is used to get a high density single scan image and making Phase Measuring Profilometry insensitive to ‘z’ motion and prevent motion banding which is predominant in 3D reconstruction when the object is in motion during the scan time