Efficient 3D Tracking for Motion Compensation in Beating Heart Surgery

International audienceThe design of physiological motion compensation systems for robotic-assisted cardiac Minimally Invasive Surgery (MIS) is a challeng- ing research topic. In this domain, vision-based techniques have proven to be a practical way to retrieve the motion of the beating heart. However due to the complexity of the heart motion and its surface characteristics, efficient tracking is still a complicated task. In this paper, we propose an algorithm for tracking the 3D motion of the beating heart, based on a Thin-Plate Splines (TPS) parametric model. The novelty of our approach lies in that no explicit matching between the stereo camera images is re- quired and consequently no intermediate steps such as rectification are needed. Experiments conducted on ex-vivo and in-vivo tissue show the effectiveness of the proposed algorithm for tracking surfaces undergoing complex deformations

Integration of New Features for Telerobotic Surgery into The Mirosurge System

International audienc

Streamline-based three-phase history matching

Geologic models derived from static data alone typically fail to reproduce the production history of a reservoir, thus the importance of reconciling simulation models to the dynamic response of the reservoir. This necessity has been the motivation behind the active research work in history matching. Traditionally, history matching is performed manually by applying local and regional changes to reservoir properties. While this is still in general practice, the subjective overtone of this approach, the time and manpower requirements, and the potential loss of geologic consistency have led to the development of a variety of alternative workflows for assisted and automatic history matching. Automatic history matching requires the solution of an inverse problem by minimizing an appropriately defined misfit function. Recent advances in geostatistics have led to the building of high-resolution geologic models consisting of millions of cells. Most of these are scaled up to the submillion size for reservoir simulation purposes. History matching even the scaled up models is computationally prohibitive. The associated cost in terms of time and manpower has led to increased interest in efficient history matching techniques and in particular, to sensitivity-based algorithms because of their rapid convergence. Furthermore, of the sensitivity-based methods, streamline-based production data integration has proven to be extremely efficient computationally. In this work, we extend the history matching capability of the streamline-based technique to three-phase production while addressing in general, pertinent issues associated with history matching. We deviate from the typical approach of formulating the inverse problem in terms of derived quantities such as GOR and Watercut, or measured phase rates, but concentrate on the fundamental variables that characterize such quantities. The presented formulation is in terms of well node saturations and pressures. Production data is transformed to composite saturation quantities, the time variation of which is matched in the calibration exercise. The dependence of the transformation on pressure highlights its importance and thus a need for pressure match. To address this need, we follow a low frequency asymptotic formulation for the pressure equation. We propose a simultaneous inversion of the saturation and pressure components to account for the interdependence and thus, high non-linearity of three phase inversion. We also account for global parameters through experimental design methodology and response surface modeling. The validity of the proposed history matching technique is demonstrated through application to both synthetic and field cases

Tracking dynamic regions of texture and shape

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 137-142).The tracking of visual phenomena is a problem of fundamental importance in computer vision. Tracks are used in many contexts, including object recognition, classification, camera calibration, and scene understanding. However, the use of such data is limited by the types of objects we are able to track and the environments in which we can track them. Objects whose shape or appearance can change in complex ways are difficult to track as it is difficult to represent or predict the appearance of such objects. Furthermore, other elements of the scene may interact with the tracked object, changing its appearance, or hiding part or all of it from view. In this thesis, we address the problem of tracking deformable, dynamically textured regions under challenging conditions involving visual clutter, distractions, and multiple and prolonged occlusion. We introduce a model of appearance capable of compactly representing regions undergoing nonuniform, nonrepeating changes to both its textured appearance and shape. We describe methods of maintaining such a model and show how it enables efficient and effective occlusion reasoning. By treating the visual appearance as a dynamically changing textured region, we show how such a model enables the tracking of groups of people. By tracking groups of people instead of each individual independently, we are able to track in environments where it would otherwise be difficult, or impossible. We demonstrate the utility of the model by tracking many regions under diverse conditions, including indoor and outdoor scenes, near-field and far-field camera positions, through occlusion and through complex interactions with other visual elements, and by tracking such varied phenomena as meteorological data, seismic imagery, and groups of people.by Joshua Migdal.Ph.D