Dataset Information

Detection of changes in luminance distributions.

ABSTRACT: How well can observers detect the presence of a change in luminance distributions? Performance was measured in three experiments. Observers viewed pairs of grayscale images on a calibrated CRT display. Each image was a checkerboard. All luminances in one image of each pair consisted of random draws from a single probability distribution. For the other image, some patch luminances consisted of random draws from that same distribution, while the rest of the patch luminances (test patches) consisted of random draws from a second distribution. The observers' task was to pick the image with luminances drawn from two distributions. The parameters of the second distribution that led to 75% correct performance were determined across manipulations of (1) the number of test patches, (2) the observers' certainty about test patch location, and (3) the geometric structure of the images. Performance improved with number of test patches and location certainty. The geometric manipulations did not affect performance. An ideal observer model with high efficiency fit the data well and a classification image analysis showed a similar use of information by the ideal and human observers, indicating that observers can make effective use of photometric information in our distribution discrimination task.

SUBMITTER: Lee TY

PROVIDER: S-EPMC3256591 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of changes in luminance distributions.

Lee Thomas Y TY Brainard David H DH

Journal of vision 20111115 13

How well can observers detect the presence of a change in luminance distributions? Performance was measured in three experiments. Observers viewed pairs of grayscale images on a calibrated CRT display. Each image was a checkerboard. All luminances in one image of each pair consisted of random draws from a single probability distribution. For the other image, some patch luminances consisted of random draws from that same distribution, while the rest of the patch luminances (test patches) consiste ...[more]

PMID: 22085597

Similar Datasets

Project description:Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy-detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time-to-detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time-to-first detection conditional on occupancy in relation to local factors, using modified interval-censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time-to-detection model provided unbiased parameter estimates despite interval-censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P-values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval-censored time-to-detection model provides a practical solution to model occupancy-detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.

Dataset Information

Detection of changes in luminance distributions.

Publications

Detection of changes in luminance distributions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets