Stochastic neural nets and vision

Thomas C. Fall

doi:10.1117/12.45516

1 March 1991 Stochastic neural nets and vision

Thomas C. Fall

Proceedings Volume 1468, Applications of Artificial Intelligence IX; (1991) https://doi.org/10.1117/12.45516
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

A stochastic neural net shares with the normally defined neural nets the concept that information is processed by a system consisting of a set of nodes (neurons) connected by weighted links (axons). The normal neural net takes in inputs on an initial layer of neurons which fire appropriately; a neuron of the next layer fires depending on the sum of weights of the axons leading to it from fired neurons of the first layer. The stochastic neural net differs in that the neurons are more complex and that the vision activity is a dynamic process. The first layer (viewing layer) of neurons fires stochastically based on the average brightness of the area it sees and then has a refractory period. The viewing layer looks at the image for several clock cycles. The effect is like those photo sensitive sunglasses that darken in bright light. The neurons over the bright areas are most likely in a refractory period (and this can't fire) and the neurons over the dark areas are not. Now if we move the sensing layer with respect to the image so that a portion of the neurons formerly over the dark are now over the bright, they will likely all fire on that first cycle. Thus, on that cycle, one would see a flash from that portion significantly stronger than surrounding regions. Movement the other direction would produce a patch that is darker, but this effect is not as noticeable. These effects are collected in a collection layer. This paper will discuss the use of the stochastic neural net for edge detection and segmentation of some simple images.

Citation Download Citation

Thomas C. Fall "Stochastic neural nets and vision", Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); https://doi.org/10.1117/12.45516

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