Lighting is one of the largest power consumers in the United States and around the globe. To better understand how much energy lighting uses in a building, a lighting audit can be performed. Typically, this is a long and manual process, current solutions require significant effort on the part of the auditor. This paper develops a system using commercially available hardware and custom algorithms that enable a single human operator to quickly cover a large area while estimating light positions, type, and surface area. These tasks are accomplished with an error rate of 6.9% and 13.9%, respectively, with surface area estimation within about a factor of two.
Perceptual Intelligence concerns the extraordinary creative genius of the mind's eye, the mind's ear, etcetera. Using π humans actively construct their perceptions of the world. We need a thorough interdisciplinary understanding of these mechanisms in order to be able to design perceptually intelligent products (including tools, systems and services). Using a new science of lighting as a vehicle, we will concretize this scientifically informed design approach, its possibilities and challenges in a dynamic complex world.
How do different object properties combine for the purposes of object identification? We developed a paradigm that allows us measure the degree to which human observers rely on one object property (e.g., color) vs. another (e.g., material) when they make forced-choice similarity judgments. On each trial of our experiment, observers viewed a target object paired with two test objects: a material match, that differed from the target only in color (along a green-blue axis) and a color match, that differed from the target only in material (along a glossy-matte axis). Across trials, the target was paired with different combinations of material-match and color-match tests and observers selected the test that appeared more similar to the target. To analyze observer responses, we developed a model (a two-dimensional generalization of the maximum-likelihood difference scaling method) that allows us to recover (1) the color-material weight, reflecting the relative importance of color vs. material in object identification and (2) the underlying positions of the material-match and color-match tests in a perceptual color-material space. Our results reveal large individual differences in the relative weighting of color vs. material.