Low thrust orbit determination program

Logical flow and guidelines are provided for the construction of a low thrust orbit determination computer program. The program, tentatively called FRACAS (filter response analysis for continuously accelerating spacecraft), is capable of generating a reference low thrust trajectory, performing a linear covariance analysis of guidance and navigation processes, and analyzing trajectory nonlinearities in Monte Carlo fashion. The choice of trajectory, guidance and navigation models has been made after extensive literature surveys and investigation of previous software. A key part of program design relied upon experience gained in developing and using Martin Marietta Aerospace programs: TOPSEP (Targeting/Optimization for Solar Electric Propulsion), GODSEP (Guidance and Orbit Determination for SEP) and SIMSEP (Simulation of SEP)

Hierarchical Estimation of Oceanic Surface Velocity Fields From Satellite Imagery.

Oceanic surface velocity fields are objectively estimated from time-sequential satellite images of sea-surface temperature from the Advanced Very High Resolution Radiometey on board the National Oceanic and Atmospheric Administration\u27s polar orbiters. The hierarchical technique uses the concept of image pyramids and multi-resolution grids for increased computational efficiency. Images are Gaussian filtered and sub-sampled from fine to coarse grid scales. The number of pyramid levels is selected such that the maximum expected velocity in the image results in a displacement of less than one pixel at the coarsest spatial scale. Maximum Cross-Correlation at the sub-pixel level with orthogonal polynomial approximation is used to compute a velocity field at each level of the pyramid which is then iterated assuming a locally linear velocity field. The first image at the next finer level of the pyramid is warped towards the second image by the calculated velocity field. At each succeeding finer grid scale, the velocity field is updated and the process repeated. The final result is an estimated velocity at each pixel at the finest resolution of the imagery. There are no free parameters as used in some gradient-based approaches and the only assumption is that the velocity field is locally linear. Test cases are shown using both simulated and real images with numerically simulated velocity fields which demonstrate the accuracy of the technique. Results are compared to gradient-based techniques using concepts of optical flow and projection onto convex sets and to the standard Maximum Cross-Correlation technique. The hierarchical computations for a real satellite image numerically advected by a rotational sheared flow recover the original field with a rms speed error of 12.6% and direction error of 4.9\sp\circ. Hierarchically-estimated velocity fields from real image pairs are compared to ground-truth estimates of the velocity from satellite-tracked drifters in the eastern Gulf of Mexico. Results indicate the technique underestimates daily mean buoy vector speeds, but with reasonably good direction. The problems of ground truth relations to hierarchically computed flows are discussed with regard to mismatches of time and space scales of measurement

Isothermal models of combustion chamber flows

Imperial Users onl

Measuring Deformations and Illumination Changes in Images with Applications to Face Recognition

This thesis explores object deformation and lighting change in images, proposing methods that account for both variabilities within a single framework. We construct a deformation- and lighting-insensitive metric that assigns a cost to a pair of images based on their similarity. The primary applications discussed will be in the domain of face recognition, because faces provide a good and important example of highly structured yet deformable objects with readily available datasets. However, our methods can be applied to any domain with deformations and lighting change. In order to model variations in expression, establishing point correspondences between faces is essential, and a primary goal of this thesis is to determine dense correspondences between pairs of face images, assigning a cost to each point pairing based on a novel image metric. We show that an image manifold can be defined to model deformations and illumination changes. Images are considered as points on a high-dimensional manifold given local structure by our new metric, where costs are based on changes in shape and intensity. Curves on this manifold describe transformations such as deformations and lighting changes to connect nearby images, or larger identity changes connecting images far apart. This allows deformations to be introduced gradually over the course of several images, where correspondences are well-defined between every pair of adjacent images along a path. The similarity between two images on the manifold can be defined as the length of the geodesic that connects them. The new local metric is validated in an optical flow-like framework where it is used to determine a dense correspondence vector field between pairs of images. We then demonstrate how to find geodesics between pairs of images on a Riemannian image manifold. The new lighting-insensitive metric is described in the wavelet domain where it is able to handle moderate amounts of deformation, and allows us to derive an algorithm where the analytic geodesics between images can be computed extremely efficiently. To handle larger deformations in addition to changes in illumination, we consider an algorithmic framework where deformations are modeled with diffeomorphisms. We present preliminary implementations of the diffeomorphic framework, and suggest how this work can be extended for further applications

Global optimization methods for full-reference and no-reference motion estimation with applications to atherosclerotic plaque motion and strain imaging

Pixel-based motion estimation using optical flow models has been extensively researched during the last two decades. The driving force of this research field is the amount of applications that can be developed with the motion estimates. Image segmentation, compression, activity detection, object tracking, pattern recognition, and more recently non-invasive biomedical applications like strain imaging make the estimation of accurate velocity fields necessary. The majority of the research in this area is focused on improving the theoretical and numerical framework of the optical flow models. This effort has resulted in increased method complexity with an increasing number of motion parameters. The standard approach of heuristically setting the motion parameters has become a major source of estimation error. This dissertation is focused in the development of reliable motion estimation based on global parameter optimization methods. Two strategies have been developed. In full-reference optimization, the assumption is that a video training set of realistic motion simulations (or ground truth) are available. Global optimization is used to calculate the best motion parameters that can then be used on a separate set of testing videos. This approach helps provide bounds on what motion estimation methods can achieve. In no-reference optimization, the true displacement field is not available. By optimizing for the agreement between different motion estimation techniques, the no-reference approach closely approximates the best (optimal) motion parameters. The results obtained with the newly developed global no-reference optimization approach agree closely with those produced with the full-reference approach. Moreover, the no-reference approach calculates velocity fields of superior quality than published results for benchmark video sequences. Unreliable velocity estimates are identified using new confidence maps that are associated with the disagreement between methods. Thus, the no-reference global optimization method can provide reliable motion estimation without the need for realistic simulations or access to ground truth. The methods developed in this dissertation are applied to ultrasound videos of carotid artery plaques. The velocity estimates are used to analyze plaque motion and produce novel non-invasive elasticity maps that can help in the identification of vulnerable atherosclerotic plaques

Confined isothermal and combusting flows behind axisymmetric baffles

Imperial Users onl

Video-based motion detection for stationary and moving cameras

In real world monitoring applications, moving object detection remains to be a challenging task due to factors such as background clutter and motion, illumination variations, weather conditions, noise, and occlusions. As a fundamental first step in many computer vision applications such as object tracking, behavior understanding, object or event recognition, and automated video surveillance, various motion detection algorithms have been developed ranging from simple approaches to more sophisticated ones. In this thesis, we present two moving object detection frameworks. The first framework is designed for robust detection of moving and static objects in videos acquired from stationary cameras. This method exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms most state-of-the-art methods. The second framework adapts moving object detection to full motion videos acquired from moving airborne platforms. This framework has two main modules. The first module stabilizes the video with respect to a set of base-frames in the sequence. The stabilization is done by estimating four-point homographies using prominent feature (PF) block matching, motion filtering and RANSAC for robust matching. Once the frame to base frame homographies are available the flux tensor motion detection module using local second derivative information is applied to detect moving salient features. Spurious responses from the frame boundaries and other post- processing operations are applied to reduce the false alarms and produce accurate moving blob regions that will be useful for tracking

Visual interpretation of Lambertian surface deformation

The major topic of this thesis is the interpretation (as a three-dimensional velocity field) of the changing intensity pattern induced by a smoothly deform¬ ing Lambertian surface of uniform albedo illuminated by a distant point light source. A constraint is derived which shows how the changing intensity pattern induced by such a deforming surface is locally constrained by the three-dimen¬ sional motion of that surface. This constraint, the "Intensity Rate Constraint", a partial differential equation in the normal component of surface velocity, con¬ tains no terms relating to the tangential components of surface velocity, hence the problem of determining the three-dimensional motion is ill-posed. The ap¬ plication of an additional constraint on the motion, (implemented in the form of a stretch-based regulariser) is proposed. This enables certain psychologi¬ cally significant classes of three-dimensional velocity field over the surface to be estimated veridically from the image intensity rate, the velocity field along the boundary and static information. This technique is successfully tested on synthetic data in experiments requiring at least ten times greater accuracy in intensity measurement than is commonly available. The thesis concludes with a suggested technique for the interpretation of smoothly deforming space-curve motion