This paper introduces the R package visualFields, a contributed, open-source software for the analysis of the visual field. The package aims to provide a framework for collaborative research, including data sharing and conventional and novel methods. Single visual field and progression analyses, such as Permutation of Pointwise Linear Regression can be performed with visualFields using simple scripts. The package can be easily customized and it allows the inclusion of custom test locations and different normative values. Here, we demonstrate how to use the visualFields package and discuss its capabilities. The analyses presented here are easy to replicate upon installation of the package, which is freely available for download from the Comprehensive R Archive Network. The relevant R code is shown and commented on. A shift from proprietary to an open-source research platform is an important step towards more direct collaborative research. The visualFields package is part of the Open Perimetry Initiative, which is expected to grow as researchers contribute new routines and datasets.

Variations in illumination on a scene and trichromatic sampling by the eye limit inferences about scene content. The aim of this work was to elucidate these limits in relation to an ideal observer using color signals alone. Simulations were based on 50 hyperspectral images of natural scenes and daylight illuminants with correlated color temperatures 4000 K, 6500 K, and 25,000 K. Estimates were made of the (Shannon) information available from each scene, the redundancies in receptoral and postreceptoral coding, and the information retrieved by an observer identifying corresponding points across image pairs. For the largest illuminant difference, between 25,000 K and 4000 K, a postreceptoral transformation providing minimum redundancy yielded an efficiency of about 80% in the information retrieved. This increased to about 89% when the transformation was optimized directly for information retrieved, corresponding to an equivalent Gaussian noise amplitude of 3.0% or to a mean of 3.6 x 10(4) distinct identifiable points per scene. Using color signals to retrieve information from natural scenes can approach ideal observer efficiency levels.

Abstract-The colors present in an image of a scene provide information about its constituent elements. But the amount of information depends on the imaging conditions and on how information is calculated. This work had two aims. The first was to derive explicitly estimators of the information available and the information retrieved from the color values at each point in images of a scene under different illuminations. The second was to apply these estimators to simulations of images obtained with five sets of sensors used in digital cameras and with the cone photoreceptors of the human eye. Estimates were obtained for 50 hyperspectral images of natural scenes under daylight illuminants with correlated color temperatures 4,000, 6,500, and 25,000 K. Depending on the sensor set, the mean estimated information available across images with the largest illumination difference varied from 15.5 to 18.0 bits and the mean estimated information retrieved after optimal linear processing varied from 13.2 to 15.5 bits (each about 85 percent of the corresponding information available). With the best sensor set, 390 percent more points could be identified per scene than with the worst. Capturing scene information from image colors depends crucially on the choice of camera sensors.