33669607
Patient-Specific Operative Planning for Aorto-Femoral Reconstruction Procedures
Traditionally, a surgeon will select a procedure for a particular patient based on past experience for patients with a similar state of disease. The experience gained from this patient will be selectively used when treating the next patient with similar symptoms. In this work, a surgical planning system was developed enabling a vascular surgeon to create and test alternative operative plans prior to surgery for a given patient. One dimensional and three dimensional hemodynamic (i.e. blood flow) simulations were performed for rest and exercise for operative plans for two aorto-femoral bypass patients and compared with actual postoperative data. The information that can be obtained from one-dimensional (volume flow distribution and pressure losses) and three-dimensional (wall shear stress) hemodynamic simulation may be clinically relevant to vascular surgeons planning interventions.
290131
Internet-based system for simulation-based medical planning for cardiovascular disease
Current practice in vascular surgery utilizes only diagnostic and empirical data to plan treatments, which does not enable quantitative a priori prediction of the outcomes of interventions. We have previously described simulation-based medical planning methods to model blood flow in arteries and plan medical treatments based on physiologic models. An important consideration for the design of these patient-specific modeling systems is the accessibility to physicians with modest computational resources. We describe a simulation-based medical planning environment developed for the World Wide Web (WWW) using the Virtual Reality Modeling Language (VRML) and the Java programming language.
8182294
Compensating for Ensemble-Specificity Effects when Building Facial Models
When attempting to code faces for modeling or recognition, estimates of dimensions are typically obtained from an ensemble. These tend to be significantly sub-optimal. Firstly, ensembles are rarely balanced with regard to identity and expression. This can be overcome by dividing the ensemble by type of variation and rotating sub-spaces relative to one another. Secondly, each face contains both predictable and non-predictable qualities; only the predictable aspects are useful for defining coding systems for other faces. Variance-based methods of defining codes (PCA) will provide eigenvectors which are themselves potential faces. Predictable aspects will induce eigenvectors with comparable levels of spatial redundancy to the ensemble. We show that this gives relatively short and consistent codes, and allows fast and accurate fitting of codes to faces.
15498523
A framework for sensor planning and control with applications to vision guided multi-robot systems
The paper presents an approach to the problem of controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem and a practical computational strategy, inspired by work on particle filtering, for implementing the approach. The ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.
14456844
Exploration strategies for mobile robots
The problem of programming a robot to carry out a systematic exploration of its environment using realistic sensors is considered. The robot is modeled as a single point moving in a two-dimensional configuration space populated with visually opaque and transparent obstacles. The robot is equipped with proximity sensors, a vision-based recognition system, and a method of odometry, all of which have some uncertainty associated with their measurements. By using visually distinctive configurations of features in the world as natural landmarks, a series of local maps is constructed. The geometrical relationships between mutually visible landmarks are used to build a relational map from this collection of local maps. This representation forms the basis for a systematic exploration algorithm. The approach is implemented in simulation, and results are presented.<<ETX>>
15389117
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
A framework for automatic landmark identification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models, known as point distribution models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation.
11355264
Automatic Model Selection by Modelling the Distribution of Residuals
Many problems in computer vision involve a choice of the most suitable model for a set of data. Typically one wishes to choose a model which best represents the data in a way that generalises to unseen data without overfitting. We propose an algorithm in which the quality of a model match can be determined by calculating how well the distribution of model residuals matches a distribution estimated from the noise on the data. The distribution of residuals has two components - the measurement noise, and the noise caused by the uncertainty in the model parameters. If the model is too complex to be supported by the data, then there will be large uncertainty in the parameters. We demonstrate that the algorithm can be used to select appropriate model complexity in a variety of problems, including polynomial fitting, and selecting the number of modes to match a shape model to noisy data.