Corrada-Emmanuel, Andres

Profile Picture
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Research Fellow, Department of Computer Science
Last Name
Corrada-Emmanuel
First Name
Andres
Discipline
Computer Sciences
Expertise
Introduction
The papers presented are concerned with the application of statistical methods to tasks in map-making and pattern recognition in machine learning.
Name

Filters

20082
Reset filters

Settings

Citations

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Autonomous estimates of horizontal decorrelation lengths for digital elevation models
    (2008-01-16) Corrada-Emmanuel, Andres; Schultz, Howard
    The precision errors in a collection of digital elevation models (DEMs) can be estimated in the presence of large but sparse correlations even when no ground truth is known. We demonstrate this by considering the problem of how to estimate the horizontal decorrelation length of DEMs produced by an automatic photogrammetric process that relies on the epipolar constraint equations. The procedure is based on a set of autonomous elevation difference equations recently proposed by us. In this paper we show that these equations can only estimate the precision errors of DEMs. The accuracy errors are unknowable since there is no ground truth. Furthermore, consideration of the invariance properties of the equations make clear that their application is limited to an imaging sensor that is accurate in its determination of the vertical direction. The practicality of the algorithm for estimating the horizontal decorrelation length of precision errors is shown by application to a set of DEMs produced from images of a desert terrain.
  • Thumbnail Image
    Publication
    Autonomous geometric precision error estimation in low-level computer vision tasks
    (2008-07-05) Corrada-Emmanuel, Andrés; Schultz, Howard
    Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that employ stereo matching algorithms to triangulate real-world points. This sparsity, coupled with a geometric theory of errors recently developed by the authors, allows for autonomous agents to calculate their own precision independently of ground truth. We connect these developments with recent advances in the mathematics of sparse signal reconstruction or compressed sensing. The theory presented here extends the autonomy of 3-D model reconstructions discovered in the 1990s to their errors.