Class of detail-controllable edge-detecting operator

Zheng Tan; Shuanhu Wu

doi:10.1117/12.380566

30 March 2000 Class of detail-controllable edge-detecting operator

Zheng Tan, Shuanhu Wu

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380566
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

The basic idea of theory of Marr's image edge-detecting is firstly to smooth original image with Gaussian function, then obtain the zero-cross map of Laplacian's transform of smoothed image. However, the residual between the original image and smoothed image remain s some feature points that may not be detected. Therefore, this paper firstly proposed a new smoothing operator which has low-pass characteristics similar to a Butterworth filter and limited spatial extent similar to a Gaussian function, then we constructed a class of edge-detecting operator that can be controlled more easily using Laplace's transform. The new edge-detecting operator also has closed forms that facilitate implementation, and allows us flexibility control feature- detecting accuracy compared to Marr's operator. In addition, Marr's edge-detecting operator is a special formulation of a new operator. Practical numerical experimental results showed that hose edge-detecting operators have some practical effect and reference value.

Citation Download Citation

Zheng Tan and Shuanhu Wu "Class of detail-controllable edge-detecting operator", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380566

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