Recursive Estimation in Econometrics

An account is given of recursive regression and of Kalman filtering which gathers the important results and the ideas that lie behind them within a small compass. It emphasises the areas in which econometricians have made contributions, which include the methods for handling the initial-value problem associated with nonstationary processes and the algorithms of fixed-interval smoothing.Recursive regression, Kalman filtering, Fixed-interval smoothing, The initial-value problem

A Stochastic Modeling Approach to Region-and Edge-Based Image Segmentation

The purpose of image segmentation is to isolate objects in a scene from the background. This is a very important step in any computer vision system since various tasks, such as shape analysis and object recognition, require accurate image segmentation. Image segmentation can also produce tremendous data reduction. Edge-based and region-based segmentation have been examined and two new algorithms based on recent results in random field theory have been developed. The edge-based segmentation algorithm uses the pixel gray level intensity information to allocate object boundaries in two stages: edge enhancement, followed by edge linking. Edge enhancement is accomplished by maximum energy filters used in one-dimensional bandlimited signal analysis. The issue of optimum filter spatial support is analyzed for ideal edge models. Edge linking is performed by quantitative sequential search using the Stack algorithm. Two probabilistic search metrics are introduced and their optimality is proven and demonstrated on test as well as real scenes. Compared to other methods, this algorithm is shown to produce more accurate allocation of object boundaries. Region-based segmentation was modeled as a MAP estimation problem in which the actual (unknown) objects were estimated from the observed (known) image by a recursive classification algorithms. The observed image was modeled by an Autoregressive (AR) model whose parameters were estimated locally, and a Gibbs-Markov random field (GMRF) model was used to model the unknown scene. A computational study was conducted on images having various types of texture images. The issues of parameter estimation, neighborhood selection, and model orders were examined. It is concluded that the MAP approach for region segmentation generally works well on images having a large content of microtextures which can be properly modeled by both AR and GMRF models. On these texture images, second order AR and GMRF models were shown to be adequate

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Digital Signal Processing

Contains an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)Amoco Foundation FellowshipU.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)National Science Foundation (Grant ECS80-07102)U.S. Army Research Office (Contract DAAG29-81-K-0073)Hughes Aircraft Company FellowshipAmerican Edwards Labs. GrantWhitaker Health Sciences FundPfeiffer Foundation GrantSchlumberger-Doll Research Center FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)National Science Foundation (Grant ECS79-15226)Hertz Foundation Fellowshi

Digital signal processing algorithms and structures for adaptive line enhancing

Imperial Users onl

Image analysis, modeling, enhancement, restoration, feature extraction and their applications in nondestructive evaluation and radio astronomy

The principal topic of this dissertation is the development and application of signal and image processing to Nondestructive Evaluation (NDE) and radio astronomy;The dissertation consists of nine papers published or submitted for publication. Each of them has a specific and unique topic related to signal processing or image processing in NDE or radio astronomy. Those topics are listed in the following. (1) Time series analysis and modeling of Very Large Array (VLA) phase data. (2) Image analysis, feature extraction and various applied enhancement methods for industrial NDE X-ray radiographic images. (3) Enhancing NDE radiographic X-ray images by adaptive regional Kalman filtering. (4) Robotic image segmentation, modeling, and restoration with a rule based expert system. (5) Industrial NDE radiographic X-ray image modeling and Kalman filtering considering signal-dependent colored noise. (6) Computational study of Kalman filtering VLA phase data and its computational performance on a supercomputer. (7) A practical and fast maximum entropy deconvolution method for deblurring industrial NDE X-ray and infrared images. (8) Local feature enhancement of synthetic radio images by adaptive Kalman filtering. (9) A new technique for correcting phase data of a synthetic-aperture antenna array