Images and videos
Low-light images and video footage often exhibit issues due to the interplay of various parameters such as aperture, shutter speed, and ISO settings. These interactions can lead to distortions, especially in extreme lighting conditions. This distortion is primarily caused by the inverse relationship between decreasing light intensity and increasing photon noise, which gets amplified with higher sensor gain. Additionally, secondary characteristics like white balance and color effects can also be adversely affected and may require post-processing correction.
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Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
In this work, we introduce a large-scale, Bilingual, Open World Video text benchmark dataset (BOVText V2). There are four
features for BOVText V2. Firstly, we provide 2,000+ videos with more than 1,750,000+ frames, 25 times larger than the existing
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Tea chrysanthemums can provide many components that are beneficial to human health. However, the harvesting process is time-consuming and labor-intensive. In the future, tea chrysanthemums harvesting can be done by machines. The first step towards automated harvesting is the detection of tea chrysanthemums, which are highly dependent on the quantity and quality of datasets. In a natural environment, a strain of chrysanthemum can present multiple flower heads in different stages and sizes.
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