Stripping #The Dress: the importance of contextual information on inter-individual differences in colour perception.

In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens

All the images of an outdoor scene

Abstract. The appearance of an outdoor scene depends on a variety of factors such as viewing geometry, scene structure and reflectance (BRDF or BTF), illumination (sun, moon, stars, street lamps), atmospheric condition (clear air, fog, rain) and weathering (or aging) of materials. Over time, these factors change, altering the way a scene appears. A large set of images is required to study the entire variability in scene appearance. In this paper, we present a database of high quality registered and calibrated images of a fixed outdoor scene captured every hour for over 5 months. The dataset covers a wide range of daylight and night illumination conditions, weather conditions and seasons. We describe in detail the image acquisition and sensor calibration procedures. The images are tagged with a variety of ground truth data such as weather and illumination conditions and actual scene depths. This database has potential implications for vision, graphics, image processing and atmospheric sciences and can be a testbed for many algorithms. We describe an example application- image analysis in bad weather- and show how this method can be evaluated using the images in the database. The database is available online a