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) : Shulman, David ; Brodsky, Tomas

PDF Url : ADA289057

Report Date : OCT 1994

Pagination or Media Count : 28

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.

Descriptors :   *MATHEMATICAL MODELS, *IMAGE PROCESSING, *STATISTICAL PROCESSES, ALGORITHMS, COMPUTATIONS, STOCHASTIC PROCESSES, PARAMETERS, COMPUTER PROGRAMMING, ANALYSIS OF VARIANCE, NONLINEAR SYSTEMS, IMAGE INTENSIFICATION, ERRORS, INTERPOLATION, COMPUTER GRAPHICS, COMPUTER VISION, HIERARCHIES, PIXELS.

Subject Categories : Statistics and Probability
      Computer Programming and Software

Distribution Statement : APPROVED FOR PUBLIC RELEASE