Painters reproduce some spatial statistical regularities of natural scenes. To what extent they replicate their color statistics is an open question. We investigated this question by analyzing the colors of 50 natural scenes of rural and urban environments and 44 paintings with abstract and figurative compositions. The analysis was carried out using hyperspectral imaging data from both sets and focused on the gamut and distribution of colors in the CIELAB space. The results showed that paintings, like natural scenes, have gamuts with elongated shapes in the yellow-blue direction but more tilted to the red direction. It was also found that the fraction of discernible colors, expressed as a function of the number of occurrences in the scene or painting, is well described by power laws. These have similar distribution of slopes in a log-log scale for paintings and natural scenes. These features are observed in both abstract and figurative compositions. These results suggest that the underlying chromatic structure of artistic compositions generally follows the main statistical features of the natural environment.
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http://dx.doi.org/10.1364/JOSAA.33.00A170 | DOI Listing |
BMC Biol
January 2025
Centre for Ecology & Conservation, University of Exeter, Penryn, UK.
Background: The spatial and spectral properties of the light environment underpin many aspects of animal behaviour, ecology and evolution, and quantifying this information is crucial in fields ranging from optical physics, agriculture/plant sciences, human psychophysics, food science, architecture and materials sciences. The escalating threat of artificial light at night (ALAN) presents unique challenges for measuring the visual impact of light pollution, requiring measurement at low light levels across the human-visible and ultraviolet ranges, across all viewing angles, and often with high within-scene contrast.
Results: Here, I present a hyperspectral open-source imager (HOSI), an innovative and low-cost solution for collecting full-field hyperspectral data.
Sensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
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December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
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December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
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December 2024
School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Text recognition is a rapidly evolving task with broad practical applications across multiple industries. However, due to the arbitrary-shape text arrangement, irregular text font, and unintended occlusion of font, this remains a challenging task. To handle images with arbitrary-shape text arrangement and irregular text font, we designed the Discriminative Standard Text Font (DSTF) and the Feature Alignment and Complementary Fusion (FACF).
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