In today's world, the retail fashion industry is going through radical transformations in terms of technology. The magnitude of options available on the internet makes it difficult to find suitable clothing items ...
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The widespread distribution of illegal pornographic material has become a significant societal concern due to the ease of sharing digital content over the internet. Existing detection methods often rely on manual revi...
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image harmonization is an essential technique in computer vision, aiming to generate visually consistent composite images by making the foreground compatible with the background. However, current methods primarily foc...
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ISBN:
(纸本)9798350344868;9798350344851
image harmonization is an essential technique in computer vision, aiming to generate visually consistent composite images by making the foreground compatible with the background. However, current methods primarily focus on applying a global transformation perspective, overlooking the fact that different regions in a real image can exhibit significant appearance variations. Yet, there is consistency within local regions. They also have limited representation ability by using fixed background statistics (e.g., mean, and standard deviation) for foreground normalization. Hence, we propose a hierarchical dynamics appearance translation strategy that adjusts the foreground appearance based on the corresponding background, adapting the model features and parameters from local to global view. To enhance the representation ability for targets, we employ a mixed attention mechanism for local dynamics, which adaptively modifies the features of different channels and positions. Additionally, we apply dynamic region-aware convolution guided by the foreground mask for global dynamics, which learns the adaptive representation of the foreground and background and correlations to global harmonization. To further improve the harmonization result, we integrate adversarial and perceptual loss into the model training. Experiments show our method significantly reduces parameters and achieves state-of-the-art performance compared with previous methods.
In the internet era, massive amounts of image data have em erged. How to efficiently perform accurate and efficient image retrieval from this data has become a crucial issue. Most methods are affected by the redundant...
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Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and...
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One of the image segmentation techniques, multilevel thresholding, is widely used in many computer vision applications because of its low computational complexity and efficient data representation. When it is used in ...
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ISBN:
(数字)9781665471893
ISBN:
(纸本)9781665471893
One of the image segmentation techniques, multilevel thresholding, is widely used in many computer vision applications because of its low computational complexity and efficient data representation. When it is used in cyber-physical systems and internet-of-things, a special technique is required to protect the sensitive information in an image. This paper proposes a novel homomorphic encryption (HE)-based multilevel thresholding method. To implement a comparison operation in the HE domain, which is not a basic homomorphic operation, a numerical method is adopted. Our proposed method executes comparison operations in parallel to perform more iterations and increase accuracy. When the number of iterations in the numerical comparison operation is (5, 3), the proposed three-level thresholding method shows an average peak signal-to-noise ratio of 28 dB compared to a conventional non-HE-based method and takes 3 minutes on a PC.
Space-time coding array (STCA) can realize transmitting diversity and directivity through introducing a very tiny time delay between the adjacent transmitting channels, which could utilize the high degree of freedom o...
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This paper introduces a novel approach for bandwidth extrapolation (BWE) imaging of low-frequency radar based on sparse regularization with learnable partition weights (LPWs). The proposed method enhances the quality ...
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Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such as inaccurate boundaries between pris...
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ISBN:
(纸本)9781728198354
Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such as inaccurate boundaries between pristine and forged regions, the generation of unwanted artifacts, etc. To tackle the aforementioned challenges, inspired by the high-resolution image segmentation literature, we propose a novel model called HRFNet to enable satellite image forgery localization effectively. Specifically, equipped with shallow and deep branches, our model can successfully integrate RGB and resampling features in both global and local manners to localize forgery more accurately. We perform various experiments to demonstrate that our method achieves the best performance, while the memory requirement and processing speed are not compromised compared to existing methods.
Deep joint source-channel coding (DeepJSCC) has attracted attention as a type of semantic communication that shares not only information but also meaning and intent, and it is a type of deep learning that uses an auto...
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