Titlebook: Computer Vision and Image Processing; 8th International Co Harkeerat Kaur,Vinit Jakhetiya,Sanjeev Kumar Conference proceedings 2024 The Edi

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Jan Korst,Joep van Gassel,Ruud Wijnandsproblem, where an image is given as input and intensity value is estimated as output. In the literature, various deep learning methods have been proposed for TC intensity estimation but their focus on cyclones around the Indian subcontinent is limited. We have implemented three models: regression mo

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https://doi.org/10.1007/978-3-642-61568-9age gap. Although this can be solved using data collected over long age spans, it is challenging and tedious. This work proposes a multi-scale target age-based style face ageing model using an encoder-decoder architecture to generate high-fidelity face images under ageing. Further, we propose using

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https://doi.org/10.1007/978-3-642-61568-9works. Nonetheless, the growing complexity of datasets and the ongoing pursuit of enhanced performance necessitate innovative approaches. In this study, we introduce a novel deep neural network, referred to as the “T-Fusion Net,” which incorporates multiple spatial attention mechanisms based on loca

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Computer Vision and Image Processing978-3-031-58174-8Series ISSN 1865-0929 Series E-ISSN 1865-0937

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978-3-031-58173-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/d/image/242299.jpg

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https://doi.org/10.1007/978-1-4612-0323-0intain robustness to few high-frequency corruptions at high severity levels. Analyzing the Fourier signature of those corruptions reveal a change in behavior - at high severity they corrupt low frequencies as well. A Gaussian-trained model loses its performance due to this change. Current augmentati

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Algorithms for Reinforcement Learningntation was performed on the four standard dataset like Road Damage Dataset (RDD)-2018, Road Damage Dataset (RDD)-2019, Road Damage Dataset (RDD)-2020 and Road Damage Dataset (RDD)-2022 considering the different locations and uneven illumination condition. From the study it is figured out that Co-oc