Models of human image discrimination predict object detection in natural backgrounds
Albert J. Ahumada, Andrew B. Watson, Ann Marie Rohaly · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Object detection involves looking for one of a large set of object subimages in a large set of background images. Image discrimination models predict the probability that an observer will detect a difference between two images. We find that discrimination models can predict the relative detectability of objects in different images, suggesting that these simpler models may be useful in some object detection applications. Six images of a vehicle in an otherwise natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Nineteen observers rated the 24 images for the presence of a vehicle. The pattern of observer detectabilities for the different images was predicted by three discrimination models. A Cortex transform discrimination model, a contrast sensitivity function filter model, and a root-mean-square difference predictor based on the digital image values gave prediction errors of 15%, 49%, and 46%, respectively. Two observers given the same images repeatedly to make the task a discrimination task rated the images similarly, but had detectabilities a factor of two higher.