Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results.

Modeling the motion that occurs between frames of a video sequence is a key component of video coding applications. Typically it is not possible to represent the motion between frames by a single model and therefore a quad-tree structure is employed where smaller, variable size regions or blocks are allowed to take on separate motion models. Quad-tree structures however suffer from two fundamental forms of redundancy. First, quad-trees exhibit structural redundancy due to their inability to exploit the dependence between neighboring leaf nodes with different parents. The second form of redundancy is due to the quad-tree structure itself being limited to capture only horizontal and vertical edge discontinuities at dyadically related locations; this means that general discontinuities in the motion field, such as those caused by boundaries of moving objects, become difficult and expensive to model. In our work, we address the issue of structural redundancy by introducing leaf merging. We describe how the intuitively appealing leaf merging step can be incorporated into quad-tree motion representations for a range motion modeling contexts. In particular, the impact of rate-distortion (R-D) optimized merging for two motion coding schemes, these being spatially predictive coding, as used by H.264, and hierarchical coding, are considered. Our experimental results demonstrate that the merging step can provide significant gains in R-D performance for both the hierarchical and spatial prediction schemes. Hierarchical coding has the advantage that it offers scalable access to the motion information; however due to the redundancy it introduces hierarchical coding has not been traditionally pursued. Our work shows that much of this redundancy can be mitigated with the introduction of merging. To enable scalable decoding, we employ a merging scheme which ensures that the dependencies introduced via merging can be hierarchically decoded. Theoretical investigations confirm the inherent advantages of leaf merging for quad-tree motion models. To enable quad-tree structures to better model motion discontinuity boundaries, we introduce geometry information to the quad-tree representation. We choose to model motion and geometry using separate quad-tree structures; thereby enabling each attribute to be refined separately. We extend the leaf merging paradigm to incorporate the dual tree structure allowing regions to be formed that have both motion and geometry attributes, subject to rate-distortion optimization considerations. We employ hierarchical coding for the motion and geometry information and ensure that the merging process retains the property of resolution scalability. Experimental results show that the R-D performance of the merged dual tree representation, is significantly better than conventional motion modeling schemes. Theoretical investigations show that if both motion and boundary geometry can be perfectly modeled, then the merged dual tree representation is able to achieve optimal R-D performance. We explore resolution scalability of merged quad-tree representations. We consider a modified Lagrangian cost function that takes into account the possibility of scalable decoding. Experimental results reveal that the new cost objective can considerably improve scalability performance without significant loss in overall efficiency and with competitive performance at all resolutions.

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In Inverse Synthetic Aperture Radar (ISAR) systems the motion of the target can be classified in two main categories: Translational Motion and Rotational Motion. A small degree of rotational motion is required in order to generate the synthetic aperture of the ISAR systems. On the other hand, the remaining part of the target&rsquo / s motion, that is any degree of translational motion and the large degree of rotational motion, degrades ISAR image quality. Motion compensation techniques focus on eliminating the effect of the targets&rsquo / motion on the ISAR images. In this thesis, ISAR image generation is discussed using both Conventional Fourier Based and Time-Frequency Based techniques. Standard translational motion compensation steps, Range and Doppler Tracking, are examined. Cross-correlation method and Dominant Scatterer Algorithm are employed for Range and Doppler tracking purposes, respectively. Finally, Time-Frequency based motion compensation is studied and compared with the conventional techniques. All of the motion compensation steps are examined using the simulated data. Stepped frequency waveforms are used in order to generate the required data of the simulations. Not only successful results, but also worst case examinations and lack of algorithms are also discussed with the examples.

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L'introduction de systèmes robotisés dans les salles opératoires a fait évoluer la chirurgie moderne, ouvrant aux chirurgiens de nouvelles possibilités. La présence de tels systèmes en salle opératoire croît chaque année. Les progrès des robots médicaux sont étroitement liés au développement de nouvelles techniques permettant de mieux contrôler les interactions entre la machine et les tissus biologiques. L'objectif principal de cette thèse est de proposer une commande en force basée sur un modèle, conçue pour améliorer la stabilité et la robustesse du contrôle en vue d'applications médicales. Une étude sur la modélisation des tissus mous ainsi que le choix d'un modèle compatible temps-réel sont présentés. Après cette analyse, le modèle de Kelvin Boltzmann a été choisi et implémenté dans le schéma de contrôle en force proposé, basé sur des observateurs actifs. La stabilité et la robustesse de la commande sont analysées en théorie et au travers d'expérimentations. Les performances de la commande en force sont également mesurées, en tenant compte des perturbations dues aux mouvements physiologiques. Finalement, afin d'améliorer la qualité du rejet des perturbations, une boucle de commande supplémentaire est ajoutée au moyen d'une estimation des perturbations basée sur le modèle de Kelvin Boltzmann et des séries de Fourier. / The introduction of robotic systems inside the operating room has changed the modern surgery, opening new possibilities to surgeons. The number of robotic systems inside the operation room is increasing every year. The progress of medical robots are associated to the development of new techniques to better control the interaction between the robot and living soft tissues. This thesis focus on the development of a model based force control designed to improve stability and robustness of force control addressed to medical applications. A study of soft tissue modeling is presented and a suitable model to be used in a real-time control is selected. After the analysis, the Kelvin Boltzmann model was chosen to be inserted in the proposed force control scheme based on Active Observers. Stability and robustness are theoretically and experimentally analyzed. The performance of the proposed force control is also investigated under physiological motion disturbances. At the end, to improve the disturbance rejection capability, an extra control loop is added using a disturbance estimation based on the Kelvin Boltzmann model and a Fourier series.

Dans ce travail de thèse, nous examinons les principaux facteurs affectant la transparence d'un schéma de téléopération dans le contexte de la robotique médicale.Afin de déterminer ces facteurs, une analyse approfondie de l'état de l'art a été réalisée ce qui a permis de proposer une nouvelle classification de schémas de téléopération avec retour haptique.Le rôle de ces principaux facteurs a été analysé.Ces facteurs sont liés à l'architecture de commande appliquée, aux perturbations provoquées par les mouvements physiologiques des tissus manipulés ainsi qu'à la précision du modèle d'interaction robot-tissue.Les performances du schéma de téléopération à architecture 3-canaux ont été analysées en simulation pour choisir une architecture de commande dédiée aux applications médicales.Ensuite, l'influence des mouvements physiologiques de l'environnement manipulé sur la transparence du système a été analysée et un nouveau modèle d'interaction avec des tissus mous a été proposé.Un schéma de commande de téléopération basé modèle d'interaction a été proposé en se basant sur une analyse de passivité du port d'interaction robot-environnement.Enfin, l'importance de la précision du modèle d'interaction (robot-tissue) sur la transparence du schéma de téléopération avec retour d'effort basé-modèle a été analysée.Cette analyse a été validée en théorie et expérimentalement en implémentant le modèle Hunt-Crossly dans une commande utilisant un AOB pour réaliser une téléopération avec retour haptique.En conclusion de ce travail, les résultats de cette thèse ont été discutés et les perspectives futures ont également été proposées. / This thesis investigates the major factors affecting teleoperation transparency in medical context.A wide state of art survey is carried out and a new point of view to classify haptic teleoperation literature is proposed in order to extract the decisive factors providing a transparent teleoperation.Furthermore, the roles of three aspects have been analysed.First, The role of the applied control architecture.To this aim, the performances of 3-channel teleoperation are analysed and guidelines to select a suitable control architecture for medical applications are proposed.The validation of these guidelines is illustrated through simulations.Second, the effects of motion disturbance in the manipulated environment on telepresence are analysed.Consequently, a new model of such moving environment is proposed and the applicability of the proposed model is shown through interaction port passivity investigation.Third analysed factor is the role of the interaction model accuracy on the transparency of interaction control based haptic teleoperation.This analysis is performed theoretically and experimentally by the design and implementation of Hunt-Crossly in AOB interaction control haptic teleoperation.The results are discussed and the future perspectives are proposed.

"Patient motion is a significant cause of artifacts in SPECT imaging. It is important to be able to detect when a patient undergoing SPECT imaging is stationary, and when significant motion has occurred, in order to selectively apply motion compensation. In our system, optical cameras observe reflective markers on the patient. Subsequent image processing determines the marker positions relative to the SPECT system, calculating patient motion. We use this information to decide how to aggregate detected gamma rays (events) into projection images (frames) for tomographic reconstruction. For the most part, patients are stationary, and all events acquired at a single detector angle are treated as a single frame. When a patient moves, it becomes necessary to split a frame into subframes during each of which the patient is stationary. This thesis presents a method for splitting frames based on hypothesis testing. Two competing hypotheses and probability model are designed. Whether to split frames is based on a Bayesian recursive estimation of the likelihood function. The estimation procedure lends itself to an efficient iterative implementation. We show that the frame splitting algorithm performance is good for a sample SNR. Different motion simulation cases are presented to verify the algorithm performance. This work is expected to improve the accuracy of motion compensation in clinical diagnoses."

Coronary artery disease (CAD) is one of the leading causes of death in the world. In the United States alone, it is estimated that approximately every 25 seconds, a new CAD event will occur, and approximately every minute, someone will die of one. The detection of CAD during in its early stages is very critical to reduce the mortality rates. Magnetic resonance imaging of myocardial perfusion (MR-MPI) has been receiving significant attention over the last decade due to its ability to provide a unique view of the microcirculation blood flow in the myocardial tissue through the coronary vascular network. The ability of MR-MPI to detect changes in microcirculation during early stages of ischemic events makes it a useful tool in identifying myocardial tissues that are alive but at the risk of dying. However this technique is not yet fully established clinically due to fundamental limitations imposed by the MRI device physics. The limitations of current MRI schemes often make it challenging to simultaneously achieve high spatio-temporal resolution, sufficient spatial coverage, and good image quality in myocardial perfusion MRI. Furthermore, the acquisitions are typically set up to acquire images during breath holding. This often results in motion artifacts due to improper breath hold patterns. This dissertation deals with developing novel image reconstruction methods in conjunction with non-Cartesian sampling for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. The reconstruction methods are based on adaptive signal models to represent the dynamic data using few model coefficients. Three novel adaptive reconstruction methods are developed and validated: (a) low rank and sparsity based modeling, (b) blind compressed sensing, and (c) motion compensated compressed sensing. The developed methods are applicable to a wide range of dynamic imaging problems. In the context of MR-MPI, this dissertation show feasibilities that the developed methods can enable free breathing myocardial perfusion MRI acquisitions with high spatio-temporal resolutions ( < 2mm x 2mm, 1 heart beat) and slice coverage (upto 8 slices).