Accession Number:

ADA289057

Title:

Nonlinear Scalespace via Hierarchical Statistical Modeling.

Descriptive Note:

Technical rept.,

Corporate Author:

MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH

Personal Author(s):

Report Date:

1994-10-01

Pagination or Media Count:

28.0

Abstract:

Nonlinear scalespace should be based on a hierarchical statistical model of the image intensity function. This model should contain an explicit representation of the multiscale structure of edges and corners. Using this model we can have a non-ad-hoc basis for computing the parameters we need to determine how much smoothing we should do at points that appear to be edge points. We also have a basis for computing the apparent error in our scalespace calculations. Hierarchical statistical modeling is a technique that can be applied to other problems in low-level vision, but in this introductory paper we just present the application of our scalespace theory to image smoothing.

Subject Categories:

  • Statistics and Probability
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE