The perception of color poses daunting challenges, because the light spectrum reaching the eye depends on both the reflectance of objects and the spectrum of the illuminating light source. Solving this problem requires sophisticated inferences about the properties of lighting and surfaces, and many striking examples of 'color constancy' illustrate how our vision compensates for variations in illumination to estimate the color of objects (for example [1-3]). We discovered a novel property of color perception and constancy, involving how we experience shades of blue versus yellow. We found that surfaces are much more likely to be perceived as white or gray when their color is varied along bluish directions, compared with equivalent variations along yellowish (or reddish or greenish) directions. This selective bias may reflect a tendency to attribute bluish tints to the illuminant rather than the object, consistent with an inference that indirect lighting from the sky and in shadows tends to be bluish. The blue-yellow asymmetry has striking effects on the appearance of images when their colors are reversed, turning white to yellow and silver to gold, and helps account for the variation among observers in the colors experienced in 'the dress' image that recently consumed the internet. Observers variously describe the dress as blue-black or white-gold, and this has been explained by whether the dress appears to be in direct lighting or shade (for example [5]). We show that these individual differences and potential lighting interpretations also depend on the special ambiguity of blue, for simply reversing the image colors causes almost all observers to report the lighter stripes as yellowish.
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http://dx.doi.org/10.1016/j.cub.2015.05.004 | DOI Listing |
Food Chem
December 2024
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China; China-Ireland International Cooperation Centre for Food Material Science and Structural Design, Fuzhou 350002, China.
This work investigated the effects of curdlan gum-guar gum composite microgels (CG microgels) as a fat replacer on the gel properties, water distribution, and microstructures of pork meat batters, using techniques including rheometry, SEM, and LF-NMR. Between 55 °C and 80 °C, the addition of 30 % CG microgels enhanced the viscoelastic response of pork meat batters. Additionally, the CG microgels reduced cooking loss from 18.
View Article and Find Full Text PDFAnalyst
January 2025
Questrom School of Business, Boston University, Boston, MA, 02215, USA.
Latent fingerprints (LFPs) are invisible impressions that need to be developed before being used for criminal investigation; however, existing fingerprint visualization techniques face challenges, such as complex preparation and poor contrast. To advance practical fingerprint detection, green-emissive micron-sized curcumin/kaolin composites were synthesized a facile and cost-effective one-step physical cross-linking method, which exhibited unprecedented performance in developing diversified marks, including LFPs, knuckle prints, palm prints, and footprints, with clear three-level details on various substrates. Notably, the powders successfully developed LFPs that were aged for 30 days and even up to 100 days, meeting the stringent requirements for comprehensive forensic application.
View Article and Find Full Text PDFFront Neurosci
December 2024
Munsell Color Science Laboratory, Rochester Institute of Technology, Rochester, NY, United States.
Pathologica
October 2024
Biolab, PoliToBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
Objective: Stain normalization is a technique used to standardize the color appearance of digital whole slide images (WSIs). This study aimed to assess the impact of digital stain normalization on prostate cancer diagnosis by pathologists.
Methods: A multi-institutional board of four pathologists evaluated 407 hematoxylin and eosin (H&E) prostate WSIs before and after stain normalization.
Front Plant Sci
December 2024
Jiangxi Branch of China National Tobacco Corporation, Nanchang, China.
Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages.
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