The spectral power distributions (SPD) of outdoor light sources are not constant over time and atmospheric conditions, which causes the appearance variation of a scene and common natural illumination phenomena, such as twilight, shadow, and haze/fog. Calculating the SPD of outdoor light sources at different time (or zenith angles) and under different atmospheric conditions is of interest to physically-based vision. In this paper, for computer vision and its applications, we propose a feasible, simple, and effective SPD calculating method based on analyzing the transmittance functions of absorption and scattering along the path of solar radiation through the atmosphere in the visible spectrum. Compared with previous SPD calculation methods, our model has less parameters and is accurate enough to be directly applied in computer vision. It can be applied in computer vision tasks including spectral inverse calculation, lighting conversion, and shadowed image processing. The experimental results of the applications demonstrate that our calculation methods have practical values in computer vision. It establishes a bridge between image and physical environmental information, e.g., time, location, and weather conditions.
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http://dx.doi.org/10.1364/OE.24.007266 | DOI Listing |
Sci Rep
December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
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December 2024
Department of Computer Science, The University of Hong Kong, Pokfulam Rd, Hong Kong SAR, China.
Proper exposure settings are crucial for modern machine vision cameras to accurately convert light into clear images. However, traditional auto-exposure solutions are vulnerable to illumination changes, splitting the continuous acquisition of unsaturated images, which significantly degrades the overall performance of underlying intelligent systems. Here we present the neuromorphic exposure control (NEC) system.
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