Efficient algorithm for Gaussian blur using finite-state machines

Frederick M. Waltz; John W. V. Miller

doi:10.1117/12.326976

6 October 1998 Efficient algorithm for Gaussian blur using finite-state machines

Proceedings Volume 3521, Machine Vision Systems for Inspection and Metrology VII; (1998) https://doi.org/10.1117/12.326976
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

2D Gaussian blur operations are used in many image processing applications. The execution times of these operations can be rather long, especially where large kernels are involved. Proper use of two properties of Gaussian blurs can help to reduce these long execution times: (1) Large kernels can be decomposed into the sequential application of small kernels. (2) Gaussian blurs are separable into row and column operations. This paper makes use of both of these characteristics and adds a third one: (3) The row and column operations can be formulated as finite-state machines to produce highly efficient code and, for multi-step decompositions, eliminate writing to intermediate images. This paper shows the FSM formulation of the Gaussian blur for the general case and provides examples. Speed comparisons between various implementations are provided for some of the examples. The emphasis is on software implementations, but implementations in pipelined hardware are also discussed. Straightforward extensions of these concepts to 3- and higher-dimensional image processing are also presented. Implementation techniques for DOG (Difference-of-Gaussian filters) are also provided.

Citation Download Citation

Frederick M. Waltz and John W. V. Miller "Efficient algorithm for Gaussian blur using finite-state machines", Proc. SPIE 3521, Machine Vision Systems for Inspection and Metrology VII, (6 October 1998); https://doi.org/10.1117/12.326976

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available