Passenger flow prediction is of great significance for public transportation. Most of the existing studies mainly predict the flow for a single station only extracting temporal features without considering spatial fea...
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Medical imaging technology has become one of the indispensable computer-assisted intervention methods in clinical disease diagnosis and treatment, including identifying and locating lesion areas, detecting and segment...
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Station classification plays an important role in urban intelligent transportation systems. Recently, most of studies usually choose different features to analyze the function of stations. Moreover, they usually choos...
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The segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive resea...
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Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can ef...
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Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results th...
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Weakly supervised temporal action localization (WTAL) aims to localize action instances with only video-level labels for supervision. Recent methods convert category labels to natural language through prompting and ut...
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Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and...
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The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local aver...
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The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local averaging ***,the efficient and accurate cstiinatioii of space-variant k(4rnels which adapt to image structures,and the fast realization of the corresponding space-variant bilateral filtering are challenging *** address these problems,we present a space-variant BF(SVBF).and its linear time and error-bounded acceleration ***,we accurately estimate spacevariant,anisotropic kernels that vary with image structures in linear time through structure tensor and mininnini spanning ***,we perform SVBF in linear time using two error-bounded approximation methods,namely,low-rank tensor approximation via higher-order singular value decomposition and exponential sum *** proposed SVBF can efficiently achieve good edge-preserving *** validate the advantages of the proposed filter in applications including:image denoising,image enhancement,and image focus *** results(leinonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.
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