Luminance ratios along shadow edges remain the same even when they cross reflectance borders. According to Gilchrist (1988, Perception & Psychophysics 43 415-424) this so-called ratio-invariance property is a crucial factor in the perception of shadows. However, Soranzo and Agostini (2004, Perception 33 1359-1368) suggested that in some conditions (named 'impossible shadows'), a luminance pattern might still be perceived as a shadow even if the ratio-invariance property along its edge is violated. This can occur when an edge is collinear with another edge (contextual edge) which incorporates it, shares the same polarity, and generates a larger ratio. The hypothesis that impossible shadows are actually perceived as shadows is here tested by comparing the perceptual contrast of a luminance edge in the absence of a contextual edge (control condition) to that of both possible shadow edges (where the contextual and mediating edge share the same ratio) and impossible shadow edges (where the ratio of the contextual edge is larger rather than that at the mediating edge). We found that the perceived contrast of luminance edges shrinks in both possible and impossible shadow conditions rather than in the control condition. This evidence supports the hypothesis that a luminance pattern might be perceived as a shadow even when the ratio-invariance property is violated.
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http://dx.doi.org/10.1068/p5932 | DOI Listing |
Sensors (Basel)
December 2024
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
Effective detection of the contours of cloud masks and estimation of their distribution can be of practical help in studying weather changes and natural disasters. Existing deep learning methods are unable to extract the edges of clouds and backgrounds in a refined manner when detecting cloud masks (shadows) due to their unpredictable patterns, and they are also unable to accurately identify small targets such as thin and broken clouds. For these problems, we propose MDU-Net, a multiscale dual up-sampling segmentation network based on an encoder-decoder-decoder.
View Article and Find Full Text PDFSci Data
December 2024
Texas A&M University, Petroleum Engineering, Doha, Qatar.
Data Brief
December 2024
Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia.
The dataset presents raw data on the egocentric (first-person view) and exocentric (third-person view) perspectives, including 47166 frame images. Egocentric and exocentric frame images are recorded from original iPhone videos simultaneously. The egocentric view captures the details of proximity hand gestures and attentiveness of the iPhone wearer, while the exocentric view captures the hand gestures in the top-down view of all participants.
View Article and Find Full Text PDFPeerJ Comput Sci
August 2024
Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.
With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models EfficientDet and YOLOv8.
View Article and Find Full Text PDFJ Infect Dev Ctries
July 2024
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Introduction: Japanese spotted fever (JSF) mainly occurs in Japan; however, it has been increasingly reported in China. JSF is typically characterized by fever, rash, and eschar, in addition to non-specific symptoms. Yet, reports on the pulmonary indicators in JSF are limited.
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