Numerous fields, such as ophthalmology, optical communication, microscopy, and astronomy frequently utilize sensorless wavefront sensing (SLWS) technologies. SLWS technologies are very helpful in correcting wavefront ...
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Numerous fields, such as ophthalmology, optical communication, microscopy, and astronomy frequently utilize sensorless wavefront sensing (SLWS) technologies. SLWS technologies are very helpful in correcting wavefront aberrations without the use of dedicated sensors, Future real-time wavefront sensing technologies and algorithms will surely evolve toward sensorless possibilities. However, there are several challenges associated with aberration correction, including multiple image recording and analysis, noisy and insufficient data, data visualization, and system calibration. This study aims to provide an in-depth analysis of SLWS, including its applications, opportunities, challenges, and state-of-the-art techniques. The various SLWS algorithms are discussed, along with their benefits and drawbacks. The results of SLWS can be beneficial in applications where precise wavefront correction is essential for obtaining clear and detailed observations, such as in astronomical imaging or high-resolution microscopy.
Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successf...
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ISBN:
(纸本)9781479999880
Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successful image recommendation. The context information is expressed frequently in terms of high-order relations, such as the relations among users, tags, and images. Hypergraphs can model the aforementioned high-order relations between their vertices (i.e., users, user social groups, tags, geo-tags, and images) by hyperedges, whose influence can be assessed by properly estimating their weights. Here, an efficient adaptive hypergraph weight estimation is proposed for image tagging. In particular, both equality and inequality constraints enforced during hypergraph learning are taken into account and an efficient adaptation step selection using the Armijo rule is proposed. Experiments conducted on a dataset demonstrate the superior performance of the proposed approach compared to the state-of-the-art.
Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successf...
详细信息
ISBN:
(纸本)9781479999897
Social media sharing platforms enable image content as well as context information (e.g., user friendships, geo-tags assigned to images) to be jointly analyzed in order to achieve accurate image annotation or successful image recommendation. The context information is expressed frequently in terms of high-order relations, such as the relations among users, tags, and images. Hypergraphs can model the aforementioned high-order relations between their vertices (i.e., users, user social groups, tags, geo-tags, and images) by hyper-edges, whose influence can be assessed by properly estimating their weights. Here, an efficient adaptive hypergraph weight estimation is proposed for image tagging. In particular, both equality and inequality constraints enforced during hypergraph learning are taken into account and an efficient adaptation step selection using the Armijo rule is proposed. Experiments conducted on a dataset demonstrate the superior performance of the proposed approach compared to the state-of-the-art.
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