Patient-specific mitral valve closure prediction using 3D echocardiography.

This article presents an approach to modeling the closure of the mitral valve using patient-specific anatomical information derived from 3D transesophageal echocardiography (TEE). Our approach uses physics-based modeling to solve for the stationary configuration of the closed valve structure from the patient-specific open valve structure, which is recovered using a user-in-the-loop, thin-tissue detector segmentation. The method uses a tensile shape-finding approach based on energy minimization. This method is employed to predict the aptitude of the mitral valve leaflets to coapt. We tested the method using 10 intraoperative 3D TEE sequences by comparing the closed valve configuration predicted from the segmented open valve with the segmented closed valve, taken as ground truth. Experiments show promising results, with prediction errors on par with 3D TEE resolution and with good potential for applications in pre-operative planning.

Distributed consensus on camera pose.

Our work addresses pose estimation in a distributed camera framework. We examine how processing cameras can best reach a consensus about the pose of an object when they are each given a model of the object, defined by a set of point coordinates in the object frame of reference. The cameras can only see a subset of the object feature points in the midst of background clutter points, not knowing which image points match with which object points, nor which points are object points or background points. The cameras individually recover a prediction of the object's pose using their knowledge of the model, and then exchange information with their neighbors, performing consensus updates locally to obtain a single estimate consistent across all cameras, without requiring a common centralized processor. Our main contributions are: 1) we present a novel algorithm performing consensus updates in 3-D world coordinates penalized by a 3-D model, and 2) we perform a thorough comparison of our method with other current consensus methods. Our method is consistently the most accurate, and we confirm that the existing consensus method based upon calculating the Karcher mean of rotations is also reliable and fast. Experiments on simulated and real imagery are reported.

Geometric rectification of camera-captured document images.

Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. However, camera-captured documents may suffer from distortions caused by non-planar document shape and perspective projection, which lead to failure of current OCR technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.

Measurement of body segment mass, center of gravity, and determination of moments of inertia by double pendulum in Lemur fulvus.

The collection of data on physical parameters of body segments is a preliminary critical step in studying the biomechanics of locomotion. Little data on nonhuman body segment parameters has been published. The lack of standardization of techniques for data collection and presentation has made the comparative use of these data difficult and at times impossible. This study offers an approach for collecting data on center of gravity and moments of inertia for standardized body segments. The double swing pendulum approach is proposed as a solution for difficulties previously encountered in calculating moments of inertia for body segments. A format for prompting a computer to perform these calculations is offered, and the resulting segment mass data for Lemur fulvus is presented.