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Image Discrimination Models Predict Object Detection in Natural BackgroundsObject detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.
Document ID
20020023248
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Ahumada, Albert J., Jr.
(NASA Ames Research Center Moffett Field, CA United States)
Rohaly, A. M.
(Army Research Lab. Aberdeen Proving Ground, MD United States)
Watson, Andrew B.
(NASA Ames Research Center Moffett Field, CA United States)
Null, Cynthia H.
Date Acquired
August 20, 2013
Publication Date
January 1, 1994
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Annual Meeting of the Association for Research in Vision and Ophthalmology
Location: Fort Lauderdale, FL
Country: United States
Start Date: May 14, 1995
End Date: May 19, 1995
Sponsors: Association for Research in Vision and Ophthalmology
Funding Number(s)
PROJECT: RTOP 505-64-53
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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