Technique to extract relevant image features for visual tasks

Bettina L. Beard; Albert J. Ahumada Jr.

doi:10.1117/12.320099

17 July 1998 Technique to extract relevant image features for visual tasks

Bettina L. Beard, Albert J. Ahumada Jr.

Proceedings Volume 3299, Human Vision and Electronic Imaging III; (1998) https://doi.org/10.1117/12.320099
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

Here we demonstrate a method for constructing stimulus classification images. These images provide information regarding the stimulus aspects the observer uses to segregate images into discrete response categories. Data are first collected on a discrimination task containing low contrast noise. The noises are then averaged separately for the stimulus-response categories. These averages are then summed with appropriate signs to obtain an overall classification image. We determine stimulus classification images for a vernier acuity task to visualize the stimulus features used to make these precise position discriminations. The resulting images reject the idea that the discrimination is performed by the single best discriminating cortical unit. The classification images show one Gabor-like filter for each line, rejecting the nearly ideal assumption of image discrimination models predicting no contribution from the fixed vernier line.

Citation Download Citation

Bettina L. Beard and Albert J. Ahumada Jr. "Technique to extract relevant image features for visual tasks", Proc. SPIE 3299, Human Vision and Electronic Imaging III, (17 July 1998); https://doi.org/10.1117/12.320099

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