Accession Number:

ADA090241

Title:

Fast, Hierarchical Correlations with Gaussian-Like Kernels

Descriptive Note:

Technical rept.

Corporate Author:

MARYLAND UNIV COLLEGE PARK COMPUTER VISION LAB

Personal Author(s):

• Burt, Peter J.

Report Date:

1980-01-01

Pagination or Media Count:

59.0

Abstract:

This paper describes a new method for computing correlations which is particularly well suited for image processing. The method, called hierarchical discrete correlation, or HDC, is computationally efficient, typically requiring two or three orders of magnitude fewer computational steps than direct correlation or correlation computed in the spatial frequency domain using the Fast Fourier transform. In addition the method simultaneously generates correlations for kernels operators of many sizes. These kernels closely approximate the Gaussian probability distribution, so that the correlation is equivalent to low pass filtering. The operators commonly used in image processing can be readily obtained from sums and differences of Gaussian-like correlations at nearby image points.

Descriptors:

• *IMAGE PROCESSING
• *KERNEL FUNCTIONS
• CONVOLUTION
• CORRELATION TECHNIQUES
• EDGES
• FAST FOURIER TRANSFORMS
• GAUSSIAN QUADRATURE
• GRAPHS
• LAPLACE TRANSFORMATION
• LOW PASS FILTERS
• SPATIAL DISTRIBUTION

Subject Categories:

• Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE