D
David B. Cooper
Researcher at Brown University
Publications - 111
Citations - 3451
David B. Cooper is an academic researcher from Brown University. The author has contributed to research in topics: Polynomial & Estimation theory. The author has an hindex of 31, co-authored 111 publications receiving 3361 citations. Previous affiliations of David B. Cooper include Raytheon & IBM.
Papers
More filters
Journal ArticleDOI
Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation
M. Barzohar,David B. Cooper +1 more
TL;DR: The approach is to build geometric-probabilistic models for road image generation using Gibbs distributions and produces two boundaries for each road, or four boundaries when a mid-road barrier is present.
Journal ArticleDOI
Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields
Fernand S. Cohen,David B. Cooper +1 more
TL;DR: Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's, designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology.
Journal ArticleDOI
Describing complicated objects by implicit polynomials
TL;DR: This paper introduces and focuses on two problems: the representation power of closed implicit polynomials of modest degree for curves in 2-D images and surfaces in 3-D range data and the stable computationally efficient fitting of noisy data by closed explicit polynomial curves and surfaces.
Journal ArticleDOI
On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data
Ruud M. Bolle,David B. Cooper +1 more
TL;DR: The necessary techniques for optimal local parameter estimation and primitive boundary or surface type recognition for each small patch of data are developed, and optimal combining of these inaccurate locally derived parameter estimates are combined to arrive at roughly globally optimum object-position estimation.
Journal ArticleDOI
The 3L algorithm for fitting implicit polynomial curves and surfaces to data
TL;DR: This work introduces a completely new approach to fitting implicit polynomial geometric shape models to data and to studying these polynomials, which has significantly better repeatability, numerical stability, and robustness than current methods in dealing with noisy, deformed, or missing data.