Using Hopfield neural network and 2D evolutionary operators to detect image edge

Xiaoqin Yang; Ming Li

doi:10.1117/12.402641

9 October 2000 Using Hopfield neural network and 2D evolutionary operators to detect image edge

Xiaoqin Yang, Ming Li

Proceedings Volume 4221, Optical Measurement and Nondestructive Testing: Techniques and Applications; (2000) https://doi.org/10.1117/12.402641
Event: Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, 2000, Beijing, China

Abstract

This paper proposed an edge detection method using Hopfield neural networks and 2D evolutionary operators. The algorithm maps a detected image into a Hopfield neural network in such a way that each pixel corresponds to a neuron, and utilizes a population of Hopfield neural networks simultaneously. Different Hopfield neural networks have the same weights but begin to update with different initial neuron output states. In order to resolve the local minimum problem inherent in Hopfield neural networks and enhance the exploitation ability of evolutionary operation in extreme large search space, the dynamic equation of Hopfield neural network and 2D evolutionary operators are carried out alternatively during network's update procedure. The experiments have illustrated its good performance.

Citation Download Citation

Xiaoqin Yang and Ming Li "Using Hopfield neural network and 2D evolutionary operators to detect image edge", Proc. SPIE 4221, Optical Measurement and Nondestructive Testing: Techniques and Applications, (9 October 2000); https://doi.org/10.1117/12.402641

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